Build branch htrnaseq/update_craftbox with version updatecraftbox to htrnaseq on branch updatecraftbox (e6da525)

Build pipeline: viash-hub.htrnaseq.updatecraftbox-zzrhd Source commit: e6da525fc5 Source message: Merge branch 'main' into update_craftbox
2025-09-02 14:31:15 +00:00
commit 991c615670
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--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,19 @@
+target
+testData
+resources_test
+
+# Nextflow related files
+.nextflow
+.nextflow.log*
+work
+
+# Python related files
+*__pycache__*
+.venv
+
+# R related files
+.Rproj.user
+htrnaseq.Rproj
+
+# vscode
+.vscode
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -0,0 +1,310 @@
+# htrnaseq v0.11.0
+
+## Breaking changes
+
+* `runner`: feature annotation data (fData) is now output to a subfolder `fData` (PR #68).
+
+## New features
+
+* `runner`: add output results to state in order for the workflow to be used as subworkflow (PR #68).
+
+## Bug fixes
+
+* `runner`: disable `publishFilesProc` because this workflow handles publishing itself (PR #68).
+
+## Minor changes
+
+* Bump craftbox to v0.3.0 (PR #69).
+
+# htrnaseq v0.10.0
+
+## Breaking changes
+
+* `runner`: Replaced `ignore` parameter with `pools` paremeter. When set, only the selected pools are included for analysis.
+  By default all pools are selected (PR #66)
+
+## Bug fixes
+
+* Fix an error where processing FASTQ files from multiple lanes would cause an assertion error requesting the well demultiplexing
+  output to reside in one directory (PR #67).
+
+# Minor changes
+
+* `generate_well_statistics`: update base image to `python:3.13-trixie` (PR #67).
+
+# htrnaseq v0.9.1
+
+## Bug fixes
+
+* Reverted functionality to set `fastq_publish_dir` and `results_publish_dir` using fromState (PR #64).
+
+* `runner`: fix detection of FASTQ files with non-numerical characters in the sample name (PR #65).
+
+# htrnaseq v0.9.0
+
+## Breaking changes
+
+* `runner`: removed `plain_output` argument (PR #63).
+
+## Minor changes
+
+* `runner`: the `fastq_publish_dir` and `results_publish_dir` can now be set using `fromState` when using the workflow as subworkflow (PR #63).
+
+# htrnaseq v0.8.3
+
+## Minor changes
+
+* Bump craftbox to v0.2.0 (PR #62).
+
+# htrnaseq v0.8.2
+
+## Under the hood
+
+* Add the package config (`_viash.yaml`) to every component's target dir. This makes introspection from, e.g. a `runner` workflow much more robust (PR #61)
+
+# htrnaseq v0.8.1
+
+## Bug fixes
+
+* Fix an issue where the FASTQ files from different samples on the same sequencing run would overwrite each other (PR #56).
+
+## Under the hood
+
+* Moved the test resources to their new location (PR #47).
+
+## Minor changes
+
+* Bump `biobox` and `craftbox` dependencies to versions `0.3.1` and `0.2.0`, respectively (PR #60).
+
+# htrnaseq v0.8.0
+
+## New functionality
+
+* `save_params`: added a component to save workflow input parameters as yaml (PR #48).
+
+* Added `run_params` parameter to `htrnaseq` and `runner` workflows in order to save the input parameters
+  used for the workflow run (PR #48).
+  
+# htrnaseq v0.7.2
+
+## Documentation
+
+* Update README (PR #54)
+
+# htrnaseq v0.7.1
+
+## Bug fixes
+
+* Bump viash version to `0.9.4`. This adds support for nextflow versions starting major version 25.01 and 
+fixes an issue where an integer being passed to a argument with `type: double` resulted in an error (PR #51).
+
+* `reporting`: updated default colour mapping (PR #50).
+
+## Minor changes
+
+* `create_report`: bump bioconductor version to 3.21 in order to accommodate R version 4.5 (PR #52).
+
+# htrnaseq v0.7.0
+
+## Breaking changes
+
+The `runner` and `htrnaseq` workflow now output FASTQ files corresponding to the barcodes per input ID (per sequencing run). 
+Previously, when multiple input folders or multiple input FASTQ files were provided
+(for the `runner` and `htrnaseq` workflows respectively), the demultiplexed FASTQ files for these inputs were concatenated
+and provided as output. For the `htrnaseq` workflow, reads can still be combined by using a newly added `sampleID` argument.
+This means that two lists of FASTQ files can be provided for a single sample, and by assigning the same `sampleID`,
+these reads will be joined. For example, with other arguments are left out for brevity:
+
+```yaml
+- id: sample1_run1
+  input_r1: [sample_1_L001_1_R1.fastq, sample_1_L002_1_R1.fastq]
+  input_r2: [sample_1_L001_1_R2.fastq, sample_1_L002_1_R2.fastq]
+  sampleID: "sample_1"
+- id: sample1_run2
+  input_r1: [sample_1_L001_1_R1.fastq, sample_1_L002_1_R1.fastq]
+  input_r2: [sample_1_L001_1_R2.fastq, sample_1_L002_1_R2.fastq]
+  sampleID: "sample_1"
+- id: sample_2
+  input_r1: [sample_2_L001_1_R1.fastq, sample_2_L002_1_R1.fastq]
+  input_r2: [sample_2_L001_1_R2.fastq, sample_2_L002_1_R2.fastq]
+```
+
+For the runner, concatenation of data across samples is automatically inferred. Previously, multiple IDs (events) could be
+provided which were processed in parallel. This is no longer possible, as providing multiple will cause the matching
+samples for these runs to be concatenated.
+
+
+For example, the following old parameter yaml
+```yaml
+- id: run1
+  input: ["run_folder_1/", run_folder_2/]
+```
+should now be provided as: 
+```yaml
+- id: run1
+  input: "run_folder_1/"
+- id: run2
+  input: run_folder_2/
+```
+
+## Minor changes
+
+* Updated viash to `0.9.2` (PR #49)
+
+# htrnaseq v0.6.0
+
+## Breaking changes
+
+* `runner`: a subdirectory `data_processed` is now added to the output structure, in between
+  the experiment ID and the directory with the workflow date and version (PR #45).
+
+# htrnaseq v0.5.5
+
+## New functionality
+
+* Add `umi_length` parameter to the `runner` workflow (PR #46)
+
+# htrnaseq v0.5.4
+
+* Fix missing barcodes in the output from `generate_pool_statistics`, which caused an assertion error in `create_pdata`.
+  In order to resolve the issue `generate_well_statistics` now outputs results for all chromosomes/scaffolds presented by
+  the genome annotation, even when no reads were mapped to the regions in question. `generate_pool_statistics` will now
+  remove regions from the output that have not at least one counts across all barcodes (PR #44).
+
+# htrnaseq v0.5.3
+
+## Bug fixes
+
+* Fix `create_eset` component failing to create when one of the input samples has no counts (PR #43).
+
+# htrnaseq v0.5.2
+
+## Bug fixes
+
+* `create_fdata`: remove duplicate entries from feature data (PR #41).
+
+# htrnaseq v0.5.1
+
+## Bug fixes
+
+* `generate_well_statistics`: fix `ValueError` when an empty .bam file is provided as input (PR #40).
+* `create_pdata`: avoid false positive `ValueError` for non-overlapping barcodes when input
+  data contains empty (`NA`) values (PR #40).
+  
+
+# htrnaseq v0.5.0
+
+## New functionality
+
+* Added `ignore` parameter was added to the runner workflow in order to pass over certain input files
+  from the input directory (PR #39).
+
+# htrnaseq v0.4.0
+
+## Breaking changes
+
+An effort has been made to align the inputs for the `htrnaseq` and the mapping and demultiplexing of the wells, in order
+simplify running these steps as seperate steps (PR #37).
+  * Changes to the `parallel_map` component:
+    - The `barcode` argument has been renamed to `barcodesFasta` and the provided 
+      value for this argument must now be single FASTA file instead of a list of barcodes.
+    - The filenames for the provided FASTQ files must now conform to the format `{name}_R(1|2).fasta`,
+      where `{name}` is the well identifiers. The well identifiers correspond to the headers
+      of the FASTA file containing the barcodes (up untill the first whitespace).
+      Forward and reverse FASTQ files must still be provided in pairs, meaning that the order of
+      files provided to `input_r1` and `input_r2` remains important.
+    - The requirement for equal number of barcodes and FASTQ pairs to be provided has been dropped.
+      Instead, the barcodes provided with `barcodesFasta` are matched to the input FASTQ files by comparing
+      the header of the FASTA records to the file names of the provided FASTQ input files. Each barcode must
+      match exactly one FASTQ input pair (forward and reverse reads), but FASTQ files that were not matched to any
+      barcode are not processed. Basically, the barcodes fasta can now act as a filter for the FASTQ files to be mapped.
+  * The `utils/groupWells` workflow has been removed.
+  * `parallel_map_wf` has been removed as its functionality is now incomporated into the `parallel_map` component. 
+  * The `pool`, `well_id`, `barcode`, `lane`, `pair_end` and `n_wells` output arguments have been dropped from the 
+     `well_demultiplexing` workflow. This workflow now only outputs a list of demultiplexed FASTQ files.
+  * A `well_metadata` workflow has been implemented that extracts the metadata that is no longer output by the `well_demultiplexing`
+    workflow from the demultiplexed files and the barcodes FASTA.
+
+## New functionality
+
+* Multiple input directories can not be provided. The input reads from these from these directories
+  will be joined per barcode before mapping. This is useful when data has been generated using
+  multiple sequencing runs in order to increase sequencing depth (PR #38).
+
+# htrnaseq v0.3.0
+
+## New functionality
+
+* Added `umi_length` argument (PR #27).
+* Added `runner` workflow (PR #26, see below)
+
+## `runner` workflow
+
+* Removed `wellBarcodesLength` from `parallel_map` workflow (PR #27).
+
+## Major changes
+
+A runner workflows has been added, providing two additional features:
+
+1. Start from an input directory containing fastq files rather than a list of input fastq pairs.
+2. Improve the output of the workflow
+
+### Input directory
+
+It is now possible to specify a single `--input <basedir>` directory as input and the runner will extract the fastq file pairs. An error will be raised if the filename processing leads to errors.
+
+### Output
+
+The runner provides a complete different approach to output. A couple of things are important here:
+
+- Output is split up in 2 parts:
+
+    1. The well-demultiplexed fastq files (`--fastq_publish_dir`)
+    2. All the other results of the workflow (`--results_publish_dir`)
+
+- The well-demultiplexed fastq file are stored under `--fastq_publish_dir` according to the following format:
+
+    ```
+    $fastq_publish_dir/$id/<date-time>_htrnaseq_<version>/$plate_$lane/<well_id>_R1/2_001.fastq
+    ```
+
+- The other results are stored under `--results_publish_dir` according to the following format:
+
+    ```
+    $results_publish_dir/$project_id/$experiment_id/<date-time>_htrnaseq_<version>/
+    ```
+
+    This is an example listing of this directory:
+
+    ```
+    esets
+    fData
+    nrReadsNrGenesPerChrom
+    pData
+    report.html
+    star_output
+    starLogs
+    ```
+
+This output structure can be circumvented by using the `--output_dir` option, which will store all output in a single directory.
+
+1. Using the `htrnaseq` workflow directory rather than the `runner` interface
+2. Using the argument `--plain_output` with the `runner`. fastq files and other results will still be published in their respective directories, but not in a directory hierarchy as described above.
+
+## Minor changes
+
+* Use `v0.2.0` version of cutadapt instead of `main` (PR #23).
+* Use `v0.3.0` version of cutadapt
+* Bump viash to 0.9.1 (PR #31).
+* `create_eset`: Update base container image, `R` version and all dependencies
+  to newer versions (PR #28).
+
+# htrnaseq v0.2.0
+
+# New functionality
+
+* Make sure that the Well ID matches the required format (PR #22 and PR #21). 
+
+# htrnaseq v0.1.0
+
+Initial release
--- a/21
+++ b/21
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 OpenPipelines
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
--- a/README.md
+++ b/README.md
@@ -0,0 +1,207 @@
+
+
+# HT-RNAseq
+
+[![ViashHub](https://img.shields.io/badge/ViashHub-htrnaseq-7a4baa.svg)](https://www.viash-hub.com/packages/htrnaseq)
+[![GitHub](https://img.shields.io/badge/GitHub-viash--hub%2Fhtrnaseq-blue.svg)](https://github.com/viash-hub/htrnaseq)
+[![GitHub
+License](https://img.shields.io/github/license/viash-hub/htrnaseq.svg)](https://github.com/viash-hub/htrnaseq/blob/main/LICENSE)
+[![GitHub
+Issues](https://img.shields.io/github/issues/viash-hub/htrnaseq.svg)](https://github.com/viash-hub/htrnaseq/issues)
+[![Viash
+version](https://img.shields.io/badge/Viash-v0.9.4-blue.svg)](https://viash.io)
+
+## Introduction
+
+This workflow is designed to process high-throughput RNA-seq data, where
+every well of a microarray plate is a sample. A fasta file provided as
+input defines the mapping between sample barcodes and wells.
+
+The workflow is built in a modular fashion, where most of the base
+functionality is provided by components from
+[`biobox`](https://www.viash-hub.com/packages/biobox/latest)
+supplemented by custom base components and workflow components in this
+package.
+
+The full workflow is split in two major subworkflows that can be run
+independently:
+
+- **Well-demultiplexing:** Split the input (plate/pool level) fastq
+  files per well.
+- **Mapping, counting and QC:** Run per-well mapping, counting and
+  generate QC reports.
+
+Each of those can be started individually, or the full workflow can be
+run in two ways:
+
+1.  Run the [main
+    workflow](https://www.viash-hub.com/packages/htrnaseq/v0.3.0/components/workflows/htrnaseq)
+    containing the main functionality.
+2.  Run the [(opinionated)
+    `runner`](https://www.viash-hub.com/packages/htrnaseq/v0.3.0/components/workflows/runner)
+    where a number of choices (input/output structure and location) have
+    been made.
+
+Input for the workflow has to be `fastq` files (zipped or not). For bcl
+or other formats, please consider running
+[demultiplex](https://www.viash-hub.com/packages/demultiplex) first.
+
+``` mermaid lang="mermaid"
+flowchart TB
+  subgraph runner [runner]
+    direction TB
+    subgraph htrnaseq [HT-RNAseq]
+      direction LR
+      demultiplex[Well demultiplexing]
+      map
+      report
+      eset
+    end
+  end
+
+  demultiplex --> map --> report --> eset
+
+  class runner container
+  class htrnaseq container
+  class demultiplex container-inner
+  class map container-inner
+  class report container-inner
+  class eset container-inner
+
+  class demultiplex node
+  class map node
+  class report node
+  class eset node
+```
+
+## Example usage
+
+### Test and example data
+
+If you want to explore this workflow, it’s possible to the use data we
+use as test data: [a DRUGseq
+dataset](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176150)
+from the [NCBI Sequence Read Archive](https://www.ncbi.nlm.nih.gov/sra).
+For the unit and integration tests, this data has been (partly)
+subsampled to reduce the test runtime. We used
+[seqtk](https://github.com/lh3/seqtk) for this with a seed of 1, e.g.:
+
+``` bash
+seqtk sample -s1 orig/SRR14730302/VH02001614_S8_R1_001.fastq.gz 10000 > 10k/SRR14730302/VH02001614_S8_R1_001.fastq.gz
+```
+
+This data is available at: `gs://viash-hub-test-data/htrnaseq/v1/`.
+
+### Run from Viash Hub
+
+Open [Viash Hub](https://www.viash-hub.com) and browse to the [htrnaseq
+component](https://www.viash-hub.com/packages/htrnaseq/v0.3.0/components/workflows/htrnaseq).
+Press the ‘Launch’ button and follow the instructions.
+
+![](assets/htrnaseq-launch-small.png)
+
+We will start an example run loading just one input and using a barcodes
+fasta file containing only 2 wells.
+
+In the first step, we add the `local` profile to the list of profiles in
+order to limit the cpu and memory requirements of the workflow steps:
+
+![](assets/launch-parameters-1-small.png)
+
+In the next step, we provide the paramters as follows:
+
+- `input_r1`:
+  `gs://viash-hub-test-data/htrnaseq/v1/100k/SRR14730301/VH02001612_S9_R1_001.fastq`
+- `input_r2`:
+  `gs://viash-hub-test-data/htrnaseq/v1/100k/SRR14730301/VH02001612_S9_R2_001.fastq`
+- `genomeDir`:
+  `gs://viash-hub-test-data/htrnaseq/v1/genomeDir/subset/Homo_sapiens/v0.0.3/`
+- `barcodesFasta`:
+  `gs://viash-hub-test-data/htrnaseq/v1/2-wells-with-ids.fasta`
+- `annotation`:
+  `gs://viash-hub-test-data/htrnaseq/v1/genomeDir/gencode.v41.annotation.gtf.gz`
+
+Please note that both `input_r1` and `input_r2` can take multiple
+values. This means that one has to press ENTER after pasting the input
+path.
+
+![](assets/launch-parameters-2-small.png)
+
+Press the ‘Launch’ button at the end to get the instructions on how to
+run the workflow from the CLI.
+
+### Run using NF-Tower / Seqera Cloud
+
+It’s possible to run the workflow directly from [Seqera
+Cloud](https://cloud.seqera.io). The necessary [Nextflow schema
+file](https://nextflow-io.github.io/nf-schema/latest/nextflow_schema/nextflow_schema_specification/)
+has been built and provided with the workflows in order to use the
+form-based input. However, Seqera Cloud can not deal with multiple-value
+parameters when using the form-based input. Therefore, it’s better to
+use Viash Hub also here:
+
+First, select the option to run the workflow using Seqera Cloud. You
+will need to create an API token for your account. Once this token is
+filled in in the corresponding field, you will get the option to select
+a ‘Workspace’ and a ‘Compute environment’.
+
+![](assets/launch-parameters-3-small.png)
+
+Next, we need to fill in the parameters for the run. This is similar to
+before:
+
+![](assets/launch-parameters-4-small.png)
+
+In the next screen, pressing the ‘Launch’ button will actually start the
+workflow on Seqera Cloud. A message is shown when the submit was
+successful.
+
+![](assets/launch-parameters-5-small.png)
+
+### Run from the CLI
+
+Running from the CLI directly without using Viash hub is possible. The
+easiest is to just use the integrated help functionality, for instance
+using the following:
+
+``` bash
+ nextflow run https://packages.viash-hub.com/vsh/htrnaseq.git \
+  -revision v0.8.1 \
+  -main-script target/nextflow/workflows/runner/main.nf \
+  --help
+```
+
+### (Optional) Resource usage tuning
+
+Nextflow’s labels can be used to specify the amount of resources a
+process can use. This workflow uses the following labels for CPU and
+memory:
+
+- `verylowmem`, `lowmem`, `midmem`, `highmem`
+- `verylowcpu`, `lowcpu`, `midcpu`, `highcpu`
+
+The defaults for these labels can be found at
+`src/config/labels.config`. Nextflow checks that the specified resources
+for a process do not exceed what is available on the machine and will
+not start if it does. Create your own config file to tune the labels to
+your needs, for example:
+
+    // Resource labels
+    withLabel: verylowcpu { cpus = 2 }
+    withLabel: lowcpu { cpus = 8 }
+    withLabel: midcpu { cpus = 16 }
+    withLabel: highcpu { cpus = 32 }
+
+    withLabel: verylowmem { memory = { get_memory( 4.GB * task.attempt ) } }
+    withLabel: lowmem { memory = { get_memory( 8.GB * task.attempt ) } }
+    withLabel: midmem { memory = { get_memory( 16.GB * task.attempt ) } }
+    withLabel: highmem { memory = { get_memory( 64.GB * task.attempt ) } }
+
+When starting nextflow using the CLI, you can use `-c` to provide the
+file to nextflow and overwrite the defaults.
+
+## Contributions
+
+Developed in collaboration with Data Intuitive and Open Analytics.
+
+Other contributions are welcome.
--- a/README.qmd
+++ b/README.qmd
@@ -0,0 +1,149 @@
+---
+format: gfm
+---
+
+```{r setup, include=FALSE}
+project <- yaml::read_yaml("_viash.yaml")
+license <- paste0(project$links$repository, "/blob/main/LICENSE")
+contributing <- paste0(project$links$repository, "/blob/main/CONTRIBUTING.md")
+```
+
+# HT-RNAseq
+
+[![ViashHub](https://img.shields.io/badge/ViashHub-`r project$name`-7a4baa.svg)](https://www.viash-hub.com/packages/`r project$name`) 
+[![GitHub](https://img.shields.io/badge/GitHub-viash--hub%2F`r project$name`-blue.svg)](`r project$links$repository`) 
+[![GitHub License](https://img.shields.io/github/license/viash-hub/`r project$name`.svg)](`r license`) 
+[![GitHub Issues](https://img.shields.io/github/issues/viash-hub/`r project$name`.svg)](`r project$links$issue_tracker`) 
+[![Viash version](https://img.shields.io/badge/Viash-v`r gsub("-", "--", project$viash_version)`-blue.svg)](https://viash.io)
+
+## Introduction
+
+`r project$description`
+
+
+```{mermaid lang='mermaid'}
+flowchart TB
+  subgraph runner [runner]
+    direction TB
+    subgraph htrnaseq [HT-RNAseq]
+      direction LR
+      demultiplex[Well demultiplexing]
+      map
+      report
+      eset
+    end
+  end
+
+  demultiplex --> map --> report --> eset
+
+  class runner container
+  class htrnaseq container
+  class demultiplex container-inner
+  class map container-inner
+  class report container-inner
+  class eset container-inner
+
+  class demultiplex node
+  class map node
+  class report node
+  class eset node
+```
+
+
+## Example usage
+
+### Test and example data
+
+If you want to explore this workflow, it's possible to the use data we use as test data: [a DRUGseq dataset](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176150) from the [NCBI Sequence Read Archive](https://www.ncbi.nlm.nih.gov/sra). For the unit and integration tests, this data has been (partly) subsampled to reduce the test runtime. We used [seqtk](https://github.com/lh3/seqtk) for this with a seed of 1, e.g.:
+
+```bash
+seqtk sample -s1 orig/SRR14730302/VH02001614_S8_R1_001.fastq.gz 10000 > 10k/SRR14730302/VH02001614_S8_R1_001.fastq.gz
+```
+
+This data is available at: `gs://viash-hub-test-data/htrnaseq/v1/`.
+
+### Run from Viash Hub
+
+Open [Viash Hub](https://www.viash-hub.com) and browse to the [htrnaseq component](https://www.viash-hub.com/packages/htrnaseq/v0.3.0/components/workflows/htrnaseq). Press the 'Launch' button and follow the instructions.
+
+
+![](assets/htrnaseq-launch-small.png)
+
+We will start an example run loading just one input and using a barcodes fasta file containing only 2 wells.
+
+In the first step, we add the `local` profile to the list of profiles in order to limit the cpu and memory requirements of the workflow steps:
+
+
+![](assets/launch-parameters-1-small.png)
+
+In the next step, we provide the paramters as follows:
+
+- `input_r1`: `gs://viash-hub-test-data/htrnaseq/v1/100k/SRR14730301/VH02001612_S9_R1_001.fastq`
+- `input_r2`: `gs://viash-hub-test-data/htrnaseq/v1/100k/SRR14730301/VH02001612_S9_R2_001.fastq`
+- `genomeDir`: `gs://viash-hub-test-data/htrnaseq/v1/genomeDir/subset/Homo_sapiens/v0.0.3/`
+- `barcodesFasta`: `gs://viash-hub-test-data/htrnaseq/v1/2-wells-with-ids.fasta`
+- `annotation`: `gs://viash-hub-test-data/htrnaseq/v1/genomeDir/gencode.v41.annotation.gtf.gz`
+
+Please note that both `input_r1` and `input_r2` can take multiple values. This means that one has to press ENTER after pasting the input path.
+
+![](assets/launch-parameters-2-small.png)
+
+Press the 'Launch' button at the end to get the instructions on how to run the workflow from the CLI.
+
+
+### Run using NF-Tower / Seqera Cloud
+
+It's possible to run the workflow directly from [Seqera Cloud](https://cloud.seqera.io). The necessary [Nextflow schema file](https://nextflow-io.github.io/nf-schema/latest/nextflow_schema/nextflow_schema_specification/) has been built and provided with the workflows in order to use the form-based input. However, Seqera Cloud can not deal with multiple-value parameters when using the form-based input. Therefore, it's better to use Viash Hub also here:
+
+First, select the option to run the workflow using Seqera Cloud. You will need to create an API token for your account. Once this token is filled in in the corresponding field, you will get the option to select a 'Workspace' and a 'Compute environment'. 
+
+![](assets/launch-parameters-3-small.png)
+
+Next, we need to fill in the parameters for the run. This is similar to before:
+
+![](assets/launch-parameters-4-small.png)
+
+In the next screen, pressing the 'Launch' button will actually start the workflow on Seqera Cloud. A message is shown when the submit was successful.
+
+![](assets/launch-parameters-5-small.png)
+
+### Run from the CLI
+
+Running from the CLI directly without using Viash hub is possible. The easiest is to just use the integrated help functionality, for instance using the following:
+
+```bash
+ nextflow run https://packages.viash-hub.com/vsh/htrnaseq.git \
+  -revision v0.8.1 \
+  -main-script target/nextflow/workflows/runner/main.nf \
+  --help
+```
+
+### (Optional) Resource usage tuning
+
+Nextflow's labels can be used to specify the amount of resources a process can use. This workflow uses the following labels for CPU and memory:
+
+* `verylowmem`, `lowmem`, `midmem`, `highmem`
+* `verylowcpu`, `lowcpu`, `midcpu`, `highcpu`
+
+The defaults for these labels can be found at `src/config/labels.config`. Nextflow checks that the specified resources for a process do not exceed what is available on the machine and will not start if it does. Create your own config file to tune the labels to your needs, for example:
+
+```
+// Resource labels
+withLabel: verylowcpu { cpus = 2 }
+withLabel: lowcpu { cpus = 8 }
+withLabel: midcpu { cpus = 16 }
+withLabel: highcpu { cpus = 32 }
+
+withLabel: verylowmem { memory = { get_memory( 4.GB * task.attempt ) } }
+withLabel: lowmem { memory = { get_memory( 8.GB * task.attempt ) } }
+withLabel: midmem { memory = { get_memory( 16.GB * task.attempt ) } }
+withLabel: highmem { memory = { get_memory( 64.GB * task.attempt ) } }
+```
+
+When starting nextflow using the CLI, you can use `-c` to provide the file to nextflow and overwrite the defaults.
+
+## Contributions
+
+Developed in collaboration with Data Intuitive and Open Analytics.
+
+Other contributions are welcome.
--- a/_viash.yaml
+++ b/_viash.yaml
@@ -0,0 +1,21 @@
+name: htrnaseq
+summary: |
+  A workflow for high-throughput RNA-seq data analyses.
+description: "This workflow is designed to process high-throughput RNA-seq data, where every\nwell of a microarray plate is a sample. A fasta file provided as input\ndefines the mapping between sample barcodes and wells.\n\nThe workflow is built in a modular fashion, where most of the base functionality\nis provided by components from [`biobox`](https://www.viash-hub.com/packages/biobox/latest)\nsupplemented by custom base components and workflow components in this package.\n\nThe full workflow is split in two major subworkflows that can be run independently:\n\n* **Well-demultiplexing:** Split the input (plate/pool level) fastq files per well.\n* **Mapping, counting and QC:** Run per-well mapping, counting and generate QC reports.\n\nEach of those can be started individually, or the full workflow can be run in two ways:\n\n1. Run the [main workflow](https://www.viash-hub.com/packages/htrnaseq/v0.3.0/components/workflows/htrnaseq) \ncontaining the main functionality.\n2. Run the [(opinionated) `runner`](https://www.viash-hub.com/packages/htrnaseq/v0.3.0/components/workflows/runner) where a\nnumber of choices (input/output structure and location) have been made.\n\nInput for the workflow has to be `fastq` files (zipped or not). For bcl or other formats, please consider running\n[demultiplex](https://www.viash-hub.com/packages/demultiplex) first.\n"
+license: MIT
+keywords: [bioinformatics, sequencing, high-throughput, RNAseq, mapping, counting, pipeline, workflow]
+links:
+  issue_tracker: https://github.com/viash-hub/htrnaseq/issues
+  repository: https://github.com/viash-hub/htrnaseq
+viash_version: 0.9.4
+info:
+  test_resources:
+    - path: gs://viash-hub-resources/htrnaseq/v2
+      dest: resources_test
+config_mods: |
+  .requirements.commands := ['ps']
+  .runners[.type == 'nextflow'].config.script += 'includeConfig("nextflow_labels.config")'
+  .resources += {path: '/src/config/labels.config', dest: 'nextflow_labels.config'}
+  .resources += {path: '/_viash.yaml', dest: '_viash.yaml'}
+version: updatecraftbox
+organization: vsh
--- a/assets/htrnaseq-launch-small.png
+++ b/assets/htrnaseq-launch-small.png
--- a/assets/htrnaseq-launch.png
+++ b/assets/htrnaseq-launch.png
--- a/assets/launch-parameters-1-small.png
+++ b/assets/launch-parameters-1-small.png
--- a/assets/launch-parameters-1.png
+++ b/assets/launch-parameters-1.png
--- a/assets/launch-parameters-2-small.png
+++ b/assets/launch-parameters-2-small.png
--- a/assets/launch-parameters-2.png
+++ b/assets/launch-parameters-2.png
--- a/assets/launch-parameters-3-small.png
+++ b/assets/launch-parameters-3-small.png
--- a/assets/launch-parameters-3.png
+++ b/assets/launch-parameters-3.png
--- a/assets/launch-parameters-4-small.png
+++ b/assets/launch-parameters-4-small.png
--- a/assets/launch-parameters-4.png
+++ b/assets/launch-parameters-4.png
--- a/assets/launch-parameters-5-small.png
+++ b/assets/launch-parameters-5-small.png
--- a/assets/launch-parameters-5.png
+++ b/assets/launch-parameters-5.png
--- a/main.nf
+++ b/main.nf
@@ -0,0 +1,3 @@
+workflow {
+print("This is a dummy placeholder for pipeline execution. Please use the corresponding nf files for running pipelines.")
+}
--- a/nextflow.config
+++ b/nextflow.config
@@ -0,0 +1,12 @@
+manifest {
+  homePage = 'https://github.com/viash-hub/htrnaseq'
+  description = 'HT-RNAseq pipeline'
+  mainScript = 'target/nextflow/workflows/htrnaseq/main.nf'
+}
+
+process {
+  withName: publishStatesProc {
+    publishDir = [ enabled: false ]
+  }
+}
+
--- a/src/base/authors/dries_schaumont.yaml
+++ b/src/base/authors/dries_schaumont.yaml
@@ -0,0 +1,11 @@
+name: Dries Schaumont
+info:
+  links:
+    email: dries@data-intuitive.com
+    github: DriesSchaumont
+    orcid: "0000-0002-4389-0440"
+    linkedin: dries-schaumont
+  organizations:
+    - name: Data Intuitive
+      href: https://www.data-intuitive.com
+      role: Data Scientist
--- a/src/base/authors/marijke_van_moerbeke.yaml
+++ b/src/base/authors/marijke_van_moerbeke.yaml
@@ -0,0 +1,10 @@
+name: Marijke Van Moerbeke
+info:
+  links:
+    github: mvanmoerbeke
+    orcid: 0000-0002-3097-5621
+    linkedin: marijke-van-moerbeke-84303a34
+  organizations:
+    - name: OpenAnalytics
+      href: https://www.openanalytics.eu
+      role: Statistical Consultant
--- a/src/base/authors/toni_verbeiren.yaml
+++ b/src/base/authors/toni_verbeiren.yaml
@@ -0,0 +1,10 @@
+name: Toni Verbeiren
+info:
+  role: Core Team Member
+  links:
+    github: tverbeiren
+    linkedin: verbeiren
+  organizations:
+  - name: Data Intuitive
+    href: https://www.data-intuitive.com
+    role: Data Scientist and CEO
--- a/src/config/labels.config
+++ b/src/config/labels.config
@@ -0,0 +1,108 @@
+executor {
+  $k8s {
+    submitRateLimit = '10sec'
+    pollInterval = '1 sec'
+  }
+}
+
+process {
+  container = 'nextflow/bash:latest'
+  
+  // default resources
+  memory = { 8.Gb * task.attempt }
+  cpus = 8
+  maxForks = 36
+
+  // Retry for exit codes that have something to do with memory issues
+  errorStrategy = { task.exitStatus in 137..140 ? 'retry' : 'terminate' }
+  maxRetries = 3
+  maxMemory = 192.GB
+
+  // Resource labels
+  withLabel: verylowcpu { cpus = 2 }
+  withLabel: lowcpu { cpus = 8 }
+  withLabel: midcpu { cpus = 16 }
+  withLabel: highcpu { cpus = 32 }
+  
+  withLabel: verylowmem { memory = { get_memory( 4.GB * task.attempt ) } }
+  withLabel: lowmem { memory = { get_memory( 8.GB * task.attempt ) } }
+  withLabel: midmem { memory = { get_memory( 16.GB * task.attempt ) } }
+  withLabel: highmem { memory = { get_memory( 64.GB * task.attempt ) } }
+
+}
+
+profiles {
+  // detect tempdir
+  tempDir = java.nio.file.Paths.get(
+    System.getenv('NXF_TEMP') ?:
+      System.getenv('VIASH_TEMP') ?: 
+      System.getenv('TEMPDIR') ?: 
+      System.getenv('TMPDIR') ?: 
+      '/tmp'
+  ).toAbsolutePath()
+
+  mount_temp {
+    docker.temp            = tempDir
+    podman.temp            = tempDir
+    charliecloud.temp      = tempDir
+  }
+
+  no_publish {
+    process {
+      withName: '.*' {
+        publishDir = [
+          enabled: false
+        ]
+      }
+    }
+  }
+
+  docker {
+    docker.fixOwnership    = true
+    docker.enabled         = true
+    // docker.userEmulation   = true
+    singularity.enabled    = false
+    podman.enabled         = false
+    shifter.enabled        = false
+    charliecloud.enabled   = false
+  }
+
+  local {
+    // This config is for local processing.
+    process {
+        withName: ".*parallel_map_process" {
+          maxForks = 1
+        }
+        maxMemory = 25.GB
+        withLabel: verylowcpu { cpus = 2 }
+        withLabel: lowcpu { cpus = 4 }
+        withLabel: midcpu { cpus = 6 }
+        withLabel: highcpu { cpus = 8 }
+  
+        withLabel: lowmem { memory = { get_memory( 8.GB * task.attempt ) } }
+        withLabel: midmem { memory = { get_memory( 12.GB * task.attempt ) } }
+        withLabel: highmem { memory = { get_memory( 20.GB * task.attempt ) } }
+    }
+  }
+}
+
+def get_memory(to_compare) {
+    if (!process.containsKey("maxMemory") || !process.maxMemory) {
+      return to_compare
+    }
+
+    try {
+      if (process.containsKey("maxRetries") && process.maxRetries && task.attempt == (process.maxRetries as int)) {
+        return process.maxMemory
+      }
+      else if (to_compare.compareTo(process.maxMemory as nextflow.util.MemoryUnit) == 1) {
+        return max_memory as nextflow.util.MemoryUnit
+      }
+      else {
+        return to_compare
+      }  
+    } catch (all) {
+          println "Error processing memory resources. Please check that process.maxMemory '${process.maxMemory}' and process.maxRetries '${process.maxRetries}' are valid!"
+          System.exit(1)
+    }
+  }
--- a/src/eset/create_eset/config.vsh.yaml
+++ b/src/eset/create_eset/config.vsh.yaml
@@ -0,0 +1,56 @@
+name: create_eset
+namespace: "eset"
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ maintainer ]
+  - __merge__: /src/base/authors/marijke_van_moerbeke.yaml
+    roles: [ author ]
+argument_groups:
+  - name: "Arguments"
+    arguments:
+    - type: file
+      name: "--pDataFile"
+      required: true
+    - type: file
+      name: "--fDataFile"
+      required: true
+    - type: file
+      name: "--mappingDir"
+      multiple: true
+      required: true
+    - type: string
+      name: --poolName
+      required: true
+    - name: "--output"
+      type: file
+      required: true
+      direction: output
+      default: eset.$id.rds
+resources:
+  - type: r_script
+    path: script.R
+test_resources:
+  - type: r_script
+    path: test.R
+  - path: test_data/pData.tsv
+  - path: test_data/fData.tsv
+  - path: test_data/mapping_dir
+engines:
+  - type: docker
+    image: rocker/r2u:24.04
+    setup:
+      - type: r
+        cran:
+          - data.table
+          - nlcv
+        bioc:
+          - Seurat
+    test_setup:
+      - type: r
+        cran:
+          - testthat
+runners:
+  - type: executable
+  - type: nextflow
+
+      
--- a/src/eset/create_eset/script.R
+++ b/src/eset/create_eset/script.R
@@ -0,0 +1,431 @@
+library(Biobase)
+library(data.table)
+library(nlcv)
+library(Matrix)
+library(Seurat)
+
+### VIASH START
+par <- list(
+  pDataFile = "src/eset/create_eset/test_data/pData.tsv",
+  fDataFile = "src/eset/create_eset/test_data/fData.tsv",
+  studyType = "Standard",
+  mappingDir = c("src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC",
+                 "src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT"),
+  output = "eset.rds",
+  poolName = "Foo"
+)
+### VIASH END
+
+
+Read10X <- function(data_dir = NULL, gene_column = 2, unique_features = TRUE) {
+  full.data <- list()
+  for (i in seq_along(along.with = data_dir)) {
+    run <- data_dir[i]
+    if (!dir.exists(paths = run)) {
+      stop("Directory provided does not exist")
+    }
+    barcode.loc <- file.path(run, "barcodes.tsv")
+    gene.loc <- file.path(run, "features.tsv")
+    features.loc <- file.path(run, "features.tsv.gz")
+    matrix.loc <- file.path(run, "matrix.mtx")
+    pre_ver_3 <- file.exists(gene.loc)
+    if (!pre_ver_3) {
+      addgz <- function(s) {
+        return(paste0(s, ".gz"))
+      }
+      barcode.loc <- addgz(s = barcode.loc)
+      matrix.loc <- addgz(s = matrix.loc)
+    }
+    if (!file.exists(barcode.loc)) {
+      stop("Barcode file missing")
+    }
+    if (!pre_ver_3 && !file.exists(features.loc)) {
+      stop("Gene name or features file missing")
+    }
+    if (!file.exists(matrix.loc)) {
+      stop("Expression matrix file missing")
+    }
+    data <- readMM(file = matrix.loc)
+    cell.names <- readLines(barcode.loc)
+    if (all(grepl(pattern = "\\-1$", x = cell.names))) {
+      cell.names <- as.vector(x = as.character(x = sapply(X = cell.names, 
+                                                          FUN = ExtractField, field = 1, delim = "-")))
+    }
+    if (is.null(x = names(x = data_dir))) {
+      if (i < 2) {
+        colnames(x = data) <- cell.names
+      }
+      else {
+        colnames(x = data) <- paste0(i, "_", cell.names)
+      }
+    }
+    else {
+      colnames(x = data) <- paste0(names(x = data_dir)[i], 
+                                   "_", cell.names)
+    }
+    feature.names <- read.delim(file = ifelse(test = pre_ver_3, 
+                                              yes = gene.loc, no = features.loc), header = FALSE, 
+                                stringsAsFactors = FALSE)
+    if (any(is.na(x = feature.names[, gene_column]))) {
+      warning("Some features names are NA. Replacing NA names with ID from the opposite column requested", 
+              call. = FALSE, immediate. = TRUE)
+      na.features <- which(x = is.na(x = feature.names[, 
+                                                       gene_column]))
+      replacement.column <- ifelse(test = gene_column == 
+                                     2, yes = 1, no = 2)
+      feature.names[na.features, gene_column] <- feature.names[na.features, 
+                                                               replacement.column]
+    }
+    if (unique_features) {
+      fcols = ncol(x = feature.names)
+      if (fcols < gene_column) {
+        stop(paste0("gene_column was set to ", gene_column,
+                    " but feature.tsv.gz (or genes.tsv) only has ",
+                    fcols, " columns.", " Try setting the gene_column ",
+                    "argument to a value <= to ", 
+                    fcols, "."))
+      }
+      rownames(x = data) <- make.unique(names = feature.names[, 
+                                                              gene_column])
+    }
+    if (ncol(x = feature.names) > 2) {
+      data_types <- factor(x = feature.names$V3)
+      lvls <- levels(x = data_types)
+      if (length(x = lvls) > 1 && length(x = full.data) == 0) {
+        message(paste0("10X data contains more than one type and is ",
+                       "being returned as a list containing matrices ",
+                       "of each type."))
+      }
+      expr_name <- "Gene Expression"
+      if (expr_name %in% lvls) {
+        lvls <- c(expr_name, lvls[-which(x = lvls == 
+                                           expr_name)])
+      }
+      data <- lapply(X = lvls, FUN = function(l) {
+        return(data[data_types == l, , drop = FALSE])
+      })
+      names(x = data) <- lvls
+    } else {
+      data <- list(data)
+    }
+    full.data[[length(x = full.data) + 1]] <- data
+  }
+  list_of_data <- list()
+  for (j in 1:length(x = full.data[[1]])) {
+    list_of_data[[j]] <- do.call(cbind, lapply(X = full.data, 
+                                               FUN = `[[`, j))
+    list_of_data[[j]] <- as(object = list_of_data[[j]], Class = "CsparseMatrix")
+  }
+  names(x = list_of_data) <- names(x = full.data[[1]])
+  if (length(x = list_of_data) == 1) {
+    return(list_of_data[[1]])
+  } else {
+    return(list_of_data)
+  }
+}
+
+match_features <- function(exprs_matrix, fdata) {
+
+  identical_features <- all(rownames(exprs_matrix) == rownames(fdata))
+
+  if (nrow(exprs_matrix) != nrow(fdata) || !identical_features) {
+    message(paste0("Features in 'fData' and expression matrix differ. ",
+                   "Only matching features are returned."))
+  }
+
+  features <- intersect(rownames(exprs_matrix), rownames(fdata))
+  exprs_matrix <- exprs_matrix[which(rownames(exprs_matrix) %in% features), ]
+  fdata <- fdata[which(rownames(fdata) %in% features), ]
+
+  fdata[, seq_len(ncol(fdata))] <- lapply(fdata[, seq_len(ncol(fdata)), drop = FALSE], as.character)
+  # order features in exprs mat according to fdata
+  exprs_matrix <- exprs_matrix[match(rownames(fdata), rownames(exprs_matrix)), ]
+
+  list(exprs_matrix = exprs_matrix, fdata = fdata)
+
+}
+
+
+create_pdata <- function(sample_file, pool_name, barcodes) {
+  cols_to_remove <- c("SampleFileName", "Output", "Measure", "Strandedness")
+  pData <- sample_file[, !colnames(sample_file) %in% cols_to_remove,
+                       drop = FALSE]
+  rownames(pData) <- lapply(sample_file$WellBC,
+                            \(x) paste(pool_name, x, sep = "_"))
+  # pData[, ] <- lapply(pData, as.factor)
+  pData$PoolName <- pool_name
+  pData <- pData[match(barcodes, pData$WellBC), ]
+  return(pData)
+}
+
+check_sample_file <- function(mapping_dir, sample_file){
+
+  message("Checking sample annotation:")
+
+  requireNamespace("tools")
+  mapping_dir <- unlist(lapply(mapping_dir, function(x) {
+    if (!dir.exists(x)) {
+      stop(sprintf(paste0("Could not find directory ",
+                          "provided in 'mappingDir' argument (%s)."), x))
+    }
+    tools::file_path_as_absolute(x)
+  }))
+
+
+  # additional check for STARsolo
+  check_STARsolo_output <- function(x) {
+    files <- c("barcodes.tsv", "features.tsv", "matrix.mtx")
+    test <- list.files(x) %in% c(files, paste0(files, ".gz"))
+    length(test) != 0 && all(test)
+  }
+
+
+  if (!"WellBC" %in% colnames(sample_file)) {
+    stop(paste0("STARsolo output is used. The sample annotation must ",
+                "contain 'WellBC' column providing cell barcodes."))
+  }
+
+  mapping_dir <- unique(mapping_dir)
+  all_STARsolo_files_present <- all(
+    unlist(
+      lapply(mapping_dir, function(x) {
+        check_STARsolo_output(x)
+      })
+    )
+  )
+  if (!all_STARsolo_files_present) {
+    stop(paste0("Could not find files: 'barcodes', 'features' and 'matrix'",
+                " for STARsolo output. Please check 'mappingDir' argument."))
+  }
+
+  message("- 'SampleFileName' column - OK")
+
+
+
+  list(sample_expression_files = mapping_dir)
+}
+
+create_exprs_matrix <- function(exprs_matrix_path, exprs_file_paths,
+                                output, measure, col_names, cell_barcodes) {
+
+  read_matrix <- Read10X(data_dir = exprs_file_paths, gene_column = 1)
+  # keep index of feature names containing "_" because Seurat
+  #changes them to "-" and they no longer match with fdata[, "gene_id"]
+  idx <- grep("_", rownames(read_matrix))
+
+  requireNamespace("Seurat")
+  seurat_object <- Seurat::CreateSeuratObject(counts = read_matrix)
+
+  exprs_matrix <- as.matrix(seurat_object[['RNA']]$counts)
+  # replace "-" with "_" for features with "_" 
+  # before converting to Seurat object
+  rownames(exprs_matrix)[idx] <- gsub("-", "_", rownames(exprs_matrix)[idx])
+  requireNamespace("stringr")
+  exprs_matrix <- exprs_matrix[, stringr::str_detect(colnames(exprs_matrix),
+                                  paste(cell_barcodes, collapse = "|"))]
+
+
+  # check if rownames are ENSEMBL and remove version suffix
+  isENSEMBL <- all(grepl("ENS", rownames(exprs_matrix)))
+  if (isENSEMBL) {
+    # do not use gsub("(.+)[.]\\d+", "\\1", rownames(exprs_matrix)),
+    # so that ENS000000.1_PAR_Y can be kept
+    rownames(exprs_matrix) <- gsub("\\.\\d+$", "", rownames(exprs_matrix))
+  }
+
+
+  colnames(exprs_matrix) <- col_names
+
+  exprs_matrix
+}
+
+create_eset <- function(feature_annotation_path,
+                        sample_annotation_path,
+                        mapping_dir,
+                        barcodes,
+                        output_path,
+                        pool_name,
+                        exprs_matrix_path = NULL,
+                        path = NULL,
+                        add_eset_annotation = NULL) {
+  if (!file.exists(feature_annotation_path)) {
+    stop("Could not find feature annotation at '", feature_annotation_path, "'")
+  }
+
+  if (!file.exists(sample_annotation_path)) {
+    stop("Could not find sample annotation at '", sample_annotation_path, "'")
+  }
+
+  if(!is.null(exprs_matrix_path)) {
+    if(!file.exists(exprs_matrix_path)) {
+      stop("Could not find expression matrix at '", exprs_matrix_path, "'")
+    }
+  }
+
+  if(!is.null(path)) {
+    if(!dir.exists(path)) {
+      stop("Provided 'path': '", path, "' does not exist.")
+    }
+  }
+
+  ##### Import annotation files #####
+  message("Importing feature annotation")
+  fdata_file <- read.table(feature_annotation_path, header = TRUE,
+                           sep = "\t", quote = "\"",
+                           comment.char = "", stringsAsFactors = FALSE)
+
+  # for backwards compatibility
+  if("ENSEMBL" %in% colnames(fdata_file) && !all(grepl("ENS", fdata_file[, "ENSEMBL"])) & !"gene_id" %in% colnames(fdata_file)) {
+    colnames(fdata_file)[which(colnames(fdata_file) == "ENSEMBL")] <- "gene_id"
+  }
+
+  # Check gene annotation
+  if(!"gene_id" %in% colnames(fdata_file))
+    stop("'gene_id' column with unique feature identifiers must be present in 'feature_annotation_path'.")
+
+  # check if duplicated ids are present
+  if(any(duplicated(fdata_file$gene_id)))
+    stop("Duplicated features ids are not allowed. Please check the 'gene_id' column in 'feature_annotation_path'.")
+
+  message("Importing sample annotation")
+  sample_file <- read.table(sample_annotation_path, header = TRUE,
+                            sep = "\t", quote = "\"",
+                            comment.char = "", stringsAsFactors = FALSE)
+  # Check sample annotation
+  check_sample_file_list <- check_sample_file(mapping_dir = mapping_dir,
+                                              sample_file = sample_file)
+  output <- "STARsolo"
+  measure <- "counts"
+  sample_expression_files <- check_sample_file_list$sample_expression_files
+
+  ##### Create phenodata #####
+  pdata_eset <- create_pdata(sample_file = sample_file, pool_name = pool_name,
+                             barcodes = barcodes)
+
+  ##### Create expression matrix #####
+  message("Creating expression matrix")
+
+  exprs_matrix_eset <- create_exprs_matrix(
+    exprs_matrix_path = exprs_matrix_path,
+    exprs_file_paths = sample_expression_files,
+    output = output,
+    measure = measure,
+    col_names = rownames(pdata_eset),
+    cell_barcodes = barcodes
+  )
+
+
+  ##### Create featuredata #####
+  message("Creating feature data")
+
+  fdata_eset <- fdata_file
+  rownames(fdata_eset) <- fdata_eset[, "gene_id"]
+
+  # intersect features between exprs matrix and fdata
+  feature_files <- match_features(exprs_matrix = exprs_matrix_eset,
+                                  fdata = fdata_eset)
+
+  fdata_eset <- feature_files$fdata
+  exprs_matrix_eset <- feature_files$exprs_matrix
+
+  ##### Create eSet #####
+  message("Creating eset")
+
+  if (nrow(pdata_eset) != ncol(exprs_matrix_eset)) {
+    stop("nrow(pData) and ncol(exprsMatrix) differ")
+  }
+
+  if (nrow(fdata_eset) != nrow(exprs_matrix_eset)) {
+    stop("nrow(fData) and nrow(exprsMatrix) differ")
+  }
+
+  if (!all(rownames(pdata_eset) == colnames(exprs_matrix_eset))) {
+    stop("rownames(pData) and colnames(exprsMatrix) differ")
+  }
+
+  if (!all(rownames(fdata_eset) == rownames(exprs_matrix_eset))) {
+    stop("rownames(fData) and rownames(exprsMatrix) differ")
+  }
+
+  if (!inherits(exprs_matrix_eset, "matrix")) {
+    stop("exprsMatrix must be of class 'matrix'")
+  }
+
+
+
+  additional_info <- paste0("Additional information about eSet \n",
+                            "  Expression matrix created from ",
+                            output, " output. \n",
+                            "  Expression matrix contains non-transformed ",
+                            ifelse(output %in% c("STAR", "STARsolo"),
+                                   "counts",
+                                   ifelse(measure == "expected_count",
+                                          "counts", measure)), ".")
+
+
+  if (isTRUE(!is.null(add_eset_annotation) &
+               is.character(add_eset_annotation))) {
+    additional_info <- paste0(additional_info, "\n", "  ", add_eset_annotation)
+  }
+
+  fdata_eset <- new("AnnotatedDataFrame", data = fdata_eset)
+  pdata_eset <- new("AnnotatedDataFrame", data = pdata_eset)
+
+  requireNamespace("Biobase")
+  eset <- Biobase::ExpressionSet(assayData = exprs_matrix_eset,
+                                  phenoData = pdata_eset,
+                                  featureData = fdata_eset,
+                                  annotation = additional_info)
+
+  eset <- eset[, colSums(exprs(eset)) != 0]
+  saveRDS(eset, file = output_path)
+
+  message(paste0("eset created succesfully for ", ncol(eset),
+                 " samples and ", nrow(eset),
+                 " genes and saved at ", output_path, ".")) 
+
+  eset
+}
+
+
+p_data_file <- par$pDataFile
+f_data_file <- par$fDataFile
+pool_name <- par$poolName
+mapping_dir <- lapply(par$mappingDir,
+                      \(x) file.path(x, "Solo.out", "Gene", "raw"))
+
+get_barcode_from_mapping_dir <- function(raw_dir) {
+  barcodes_file <- file.path(raw_dir, "barcodes.tsv")
+  if (!file.exists(barcodes_file)) {
+    stop(paste0("Expected the 'Solo.out/Gene/raw' directory at ",
+                raw_dir, " to contain a 'barcodes.tsv' file."))
+  }
+  barcodes <- readLines(barcodes_file)
+  if (length(barcodes) != 1) {
+    stop(paste0("A single STAR Solo folder should only have ",
+                "mapped one (1) barcode, but found '",
+                length(barcodes), "'for mapping directory ", raw_dir))
+  }
+  return(barcodes)
+}
+
+barcodes <- lapply(mapping_dir, get_barcode_from_mapping_dir)
+
+print(paste0("mappingDir: ", mapping_dir))
+print(paste0("pDataFile: ", p_data_file))
+print(paste0("fDataFile: ", f_data_file))
+print(paste0("poolName: ", pool_name))
+print(paste0("barcodes: ", barcodes))
+
+
+
+# CREATE ESET WITH RAW UMI COUNTS
+
+eset <- create_eset(feature_annotation_path = f_data_file,
+                    sample_annotation_path = p_data_file,
+                    mapping_dir = mapping_dir,
+                    barcodes = barcodes,
+                    output_path = par$output,
+                    pool_name = pool_name,
+                    path = NULL,
+                    exprs_matrix_path = NULL)
--- a/src/eset/create_eset/test.R
+++ b/src/eset/create_eset/test.R
@@ -0,0 +1,132 @@
+library(testthat)
+library(Biobase)
+
+### VIASH START
+meta <- list(
+  resources_dir = "src/eset/create_eset/test_data",
+  executable = "target/executable/eset/create_eset/create_eset"
+)
+
+### VIASH END
+
+output <- tempfile()
+
+out <- processx::run(meta$executable, c(
+  "--pDataFile", file.path(meta$resources_dir, "pData.tsv"),
+  "--fDataFile", file.path(meta$resources_dir, "fData.tsv"),
+  "--mappingDir", file.path(meta$resources_dir, "mapping_dir", "AACAAGGTAC"),
+  "--mappingDir", file.path(meta$resources_dir, "mapping_dir", "ACGCCTTCGT"),
+  "--poolName", "foo",
+  "--output", output
+))
+expect_equal(out$status, 0)
+expect_true(file.exists(output))
+result <- readRDS(output)
+stopifnot(length(sampleNames(result)) == 2)
+stopifnot(all(sampleNames(result) == c("foo_AACAAGGTAC", "foo_ACGCCTTCGT")))
+expected_feature_names <- c(
+    "ENS0001058", "ENS0000221", "ENS0001387", "ENS0000508", "ENS0001199",
+    "ENS0000477", "ENS0001457", "ENS0001040", "ENS0000114", "ENS0000821",
+    "ENS0001429", "ENS0001396", "ENS0000355", "ENS0000122", "ENS0000441",
+    "ENS0001223", "ENS0001431", "ENS0000042", "ENS0000443", "ENS0000389",
+    "ENS0001208", "ENS0001140", "ENS0000071", "ENS0001369"
+)
+
+stopifnot(length(featureNames(result)) == 24)
+stopifnot(all(featureNames(result) == expected_feature_names))
+expected_expressions <- matrix(
+    c(0, 0,
+      0, 40,
+      0, 0,
+      0, 0,
+      1, 2,
+      0, 0,
+      0, 0,
+      0, 0,
+      2, 2,
+      0, 0,
+      0, 0,
+      8, 2,
+      0, 0,
+      1, 0,
+      2, 3,
+      0, 0,
+      0, 0,
+      0, 0,
+      1, 0,
+      0, 0,
+      16, 13,
+      0, 0,
+      12, 13,
+      5, 2),
+    ncol = 2,
+    nrow = 24,
+    byrow = TRUE,
+)
+rownames(expected_expressions) <- expected_feature_names
+colnames(expected_expressions) <- c("foo_AACAAGGTAC", "foo_ACGCCTTCGT")
+stopifnot(identical(exprs(result), expected_expressions))
+
+input_f_data <- read.table(file.path(meta$resources_dir, "fData.tsv"),
+                           sep = "\t", quote = "\"", comment.char = "",
+                           header = TRUE)
+input_f_data <- input_f_data[input_f_data$gene_id %in% expected_feature_names, ]
+row.names(input_f_data) <- input_f_data$gene_id
+input_f_data[] <- lapply(input_f_data, as.character)
+stopifnot(identical(input_f_data, fData(result)))
+
+# Check results filtering of barcodes with no reads
+out <- processx::run(meta$executable, c(
+  "--pDataFile", file.path(meta$resources_dir, "pData.tsv"),
+  "--fDataFile", file.path(meta$resources_dir, "fData.tsv"),
+  "--mappingDir", file.path(meta$resources_dir, "mapping_dir", "AACAAGGTAC"),
+  "--mappingDir", file.path(meta$resources_dir, "mapping_dir", "EMPTY"),
+  "--poolName", "bar",
+  "--output", output
+))
+expect_equal(out$status, 0)
+expect_true(file.exists(output))
+result <- readRDS(output)
+stopifnot(length(sampleNames(result)) == 1)
+stopifnot(all(sampleNames(result) == c("bar_AACAAGGTAC")))
+expected_feature_names <- c(
+    "ENS0001058", "ENS0000221", "ENS0001387", "ENS0000508", "ENS0001199",
+    "ENS0000477", "ENS0001457", "ENS0001040", "ENS0000114", "ENS0000821",
+    "ENS0001429", "ENS0001396", "ENS0000355", "ENS0000122", "ENS0000441",
+    "ENS0001223", "ENS0001431", "ENS0000042", "ENS0000443", "ENS0000389",
+    "ENS0001208", "ENS0001140", "ENS0000071", "ENS0001369"
+)
+stopifnot(length(featureNames(result)) == 24)
+stopifnot(all(featureNames(result) == expected_feature_names))
+expected_expressions <- matrix(
+    c(0,
+      0,
+      0,
+      0,
+      1,
+      0,
+      0,
+      0,
+      2,
+      0,
+      0,
+      8,
+      0,
+      1,
+      2,
+      0,
+      0,
+      0,
+      1,
+      0,
+      16,
+      0,
+      12,
+      5),
+    ncol = 1,
+    nrow = 24,
+    byrow = TRUE,
+)
+rownames(expected_expressions) <- expected_feature_names
+colnames(expected_expressions) <- c("bar_AACAAGGTAC")
+stopifnot(identical(exprs(result), expected_expressions))
--- a/src/eset/create_eset/test_data/fData.tsv
+++ b/src/eset/create_eset/test_data/fData.tsv
--- a/src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC/Solo.out/Gene/raw/barcodes.tsv
+++ b/src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC/Solo.out/Gene/raw/barcodes.tsv
@@ -0,0 +1 @@
+AACAAGGTAC
--- a/src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC/Solo.out/Gene/raw/features.tsv
+++ b/src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC/Solo.out/Gene/raw/features.tsv
@@ -0,0 +1,25 @@
+ENS0001140	209E3	Gene Expression
+ENS0001058	A2B9A	Gene Expression
+ENS0000508	CF168	Gene Expression
+ENS0001457	3BA5A	Gene Expression
+ENS0001431	1C968	Gene Expression
+ENS0000821	E5192	Gene Expression
+ENS0001040	1821B	Gene Expression
+ENS0000443	5AD11	Gene Expression
+ENS0000441	3F0FF	Gene Expression
+ENS0001387	265F2	Gene Expression
+ENS0001223	28A43	Gene Expression
+ENS0001208	58E28	Gene Expression
+ENS0001396	6E614	Gene Expression
+ENS0001199	EA941	Gene Expression
+ENS0001369	99DDC	Gene Expression
+ENS0000770	AFCC0	Gene Expression
+ENS0000389	B58E5	Gene Expression
+ENS0000071	7A6C3	Gene Expression
+ENS0000114	65424	Gene Expression
+ENS0000355	077A2	Gene Expression
+ENS0001429	22A4F	Gene Expression
+ENS0000477	981E6	Gene Expression
+ENS0000042	E2D99	Gene Expression
+ENS0000122	D90E9	Gene Expression
+ENS0000221	97B0F	Gene Expression
--- a/src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC/Solo.out/Gene/raw/matrix.mtx
+++ b/src/eset/create_eset/test_data/mapping_dir/AACAAGGTAC/Solo.out/Gene/raw/matrix.mtx
@@ -0,0 +1,13 @@
+%%MatrixMarket matrix coordinate integer general
+%
+25 1 10
+8 1 1
+9 1 2
+12 1 16
+13 1 8
+14 1 1
+15 1 5
+16 1 5
+18 1 12
+19 1 2
+24 1 1
--- a/src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT/Solo.out/Gene/raw/barcodes.tsv
+++ b/src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT/Solo.out/Gene/raw/barcodes.tsv
@@ -0,0 +1 @@
+ACGCCTTCGT
--- a/src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT/Solo.out/Gene/raw/features.tsv
+++ b/src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT/Solo.out/Gene/raw/features.tsv
@@ -0,0 +1,25 @@
+ENS0001140	209E3	Gene Expression
+ENS0001058	A2B9A	Gene Expression
+ENS0000508	CF168	Gene Expression
+ENS0001457	3BA5A	Gene Expression
+ENS0001431	1C968	Gene Expression
+ENS0000821	E5192	Gene Expression
+ENS0001040	1821B	Gene Expression
+ENS0000443	5AD11	Gene Expression
+ENS0000441	3F0FF	Gene Expression
+ENS0001387	265F2	Gene Expression
+ENS0001223	28A43	Gene Expression
+ENS0001208	58E28	Gene Expression
+ENS0001396	6E614	Gene Expression
+ENS0001199	EA941	Gene Expression
+ENS0001369	99DDC	Gene Expression
+ENS0000770	AFCC0	Gene Expression
+ENS0000389	B58E5	Gene Expression
+ENS0000071	7A6C3	Gene Expression
+ENS0000114	65424	Gene Expression
+ENS0000355	077A2	Gene Expression
+ENS0001429	22A4F	Gene Expression
+ENS0000477	981E6	Gene Expression
+ENS0000042	E2D99	Gene Expression
+ENS0000122	D90E9	Gene Expression
+ENS0000221	97B0F	Gene Expression
--- a/src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT/Solo.out/Gene/raw/matrix.mtx
+++ b/src/eset/create_eset/test_data/mapping_dir/ACGCCTTCGT/Solo.out/Gene/raw/matrix.mtx
@@ -0,0 +1,12 @@
+%%MatrixMarket matrix coordinate integer general
+%
+25 1 9
+9 1 3
+12 1 13
+13 1 2
+14 1 2
+15 1 2
+16 1 3
+18 1 13
+19 1 2
+25 1 40
--- a/src/eset/create_eset/test_data/mapping_dir/EMPTY/Solo.out/Gene/raw/barcodes.tsv
+++ b/src/eset/create_eset/test_data/mapping_dir/EMPTY/Solo.out/Gene/raw/barcodes.tsv
@@ -0,0 +1 @@
+CCCCCCCCCC
--- a/src/eset/create_eset/test_data/mapping_dir/EMPTY/Solo.out/Gene/raw/features.tsv
+++ b/src/eset/create_eset/test_data/mapping_dir/EMPTY/Solo.out/Gene/raw/features.tsv
@@ -0,0 +1,25 @@
+ENS0001140	209E3	Gene Expression
+ENS0001058	A2B9A	Gene Expression
+ENS0000508	CF168	Gene Expression
+ENS0001457	3BA5A	Gene Expression
+ENS0001431	1C968	Gene Expression
+ENS0000821	E5192	Gene Expression
+ENS0001040	1821B	Gene Expression
+ENS0000443	5AD11	Gene Expression
+ENS0000441	3F0FF	Gene Expression
+ENS0001387	265F2	Gene Expression
+ENS0001223	28A43	Gene Expression
+ENS0001208	58E28	Gene Expression
+ENS0001396	6E614	Gene Expression
+ENS0001199	EA941	Gene Expression
+ENS0001369	99DDC	Gene Expression
+ENS0000770	AFCC0	Gene Expression
+ENS0000389	B58E5	Gene Expression
+ENS0000071	7A6C3	Gene Expression
+ENS0000114	65424	Gene Expression
+ENS0000355	077A2	Gene Expression
+ENS0001429	22A4F	Gene Expression
+ENS0000477	981E6	Gene Expression
+ENS0000042	E2D99	Gene Expression
+ENS0000122	D90E9	Gene Expression
+ENS0000221	97B0F	Gene Expression
--- a/src/eset/create_eset/test_data/mapping_dir/EMPTY/Solo.out/Gene/raw/matrix.mtx
+++ b/src/eset/create_eset/test_data/mapping_dir/EMPTY/Solo.out/Gene/raw/matrix.mtx
@@ -0,0 +1,3 @@
+%%MatrixMarket matrix coordinate integer general
+%
+25 1 0
--- a/src/eset/create_eset/test_data/pData.tsv
+++ b/src/eset/create_eset/test_data/pData.tsv
@@ -0,0 +1,3 @@
+WellBC	WellID	NumberOfMTReads	pctMT	NumberOfERCCReads	pctERCC	NumberOfChromReads	pctChrom	NumberOfInputReads	NumberOfMappedReads	PctMappedReads	NumberOfReadsMappedToMultipleLoci	PectOfReadsMappedToMultipleLoci	NumberOfReadsMappedToTooManyLoci	PectOfReadsMappedToTooManyLoci	NumberOfReadsUnmappedTooManyMismatches	PectOfReadsUnmappedTooManyMismatches	NumberOfReadsUnmappedTooShort	PectOfReadsUnmappedTooShort	NumberOfReadsUnmappedOther	PectOfReadsUnmappedOther	ReadsWithValidBarcodes	SequencingSaturation	Q30BasesInCB+UMI	ReadsMappedToTranscriptome:Unique+MultipeGenes	EstimatedNumberOfCells	FractionOfReadsInCells	MeanReadsPerCell	NumberOfUMIs	NumberOfGenes	NumberOfCountedReads
+AACAAGGTAC	A1	0	0	0	0	8542	100	141303	23749	16.81	0	0	8458	5.99	0	0	109035	77.16	61	0.04	0.999816	0.0698056	0.979965	0.0618175	1	1	8538	7942	408	9535
+ACGCCTTCGT	B2	0	0	0	0	5863	100	96430	16869	17.49	0	0	6124	6.35	0	0	73375	76.09	62	0.06	0.999782	0.0665302	0.980077	0.0620969	1	1	5862	5472	377	6463
--- a/src/eset/create_fdata/config.vsh.yaml
+++ b/src/eset/create_fdata/config.vsh.yaml
@@ -0,0 +1,46 @@
+name: create_fdata
+namespace: eset
+description: |
+  Create a fdata file
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ maintainer ]
+  - __merge__: /src/base/authors/marijke_van_moerbeke.yaml
+    roles: [ contributor ]
+arguments:
+- name: "--gtf"
+  type: file
+  description: "Genome annotation file in GTF format."
+  required: true
+- name: "--output"
+  description: |
+    Tab-delimited text file containing information about the 'gene' or 'transcript'
+    entries from the input GTF file. The 'transcript' entries are used in case the source
+    of the GTF was 'refGene' or 'ncbiRefSeq'. 
+  type: file
+  direction: output
+  default: fData.$id.txt
+resources:
+- type: python_script
+  path: create_fdata.py
+test_resources:
+- type: python_script
+  path: test.py
+- path: test_annotation.gtf
+engines:
+  - type: docker
+    image: python:3.12-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - pandas
+    test_setup:
+      - type: python
+        packages:
+          - viashpy
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/eset/create_fdata/create_fdata.py
+++ b/src/eset/create_fdata/create_fdata.py
@@ -0,0 +1,137 @@
+import logging
+import pandas as pd
+import numpy as np
+from textwrap import fill
+
+
+### VIASH START
+meta = {
+    "name": "create_fdata",
+}
+
+par = {
+  "gtf": "src/eset/create_fdata/test_annotation.gtf",
+  "output": "fData.tsv"
+}
+
+### VIASH END
+
+logger = logging.getLogger()
+console_handler = logging.StreamHandler()
+logger.addHandler(console_handler)
+logger.setLevel(logging.DEBUG)
+
+
+def read_gtf(gtf_path: str) -> pd.DataFrame:
+    logger.info("Reading %s", gtf_path)
+    result = pd.read_csv(gtf_path, sep="\t",
+                         header=None, names=("seqname", "source",
+                                             "feature", "start", "end",
+                                             "score", "strand", "frame",
+                                             "attribute"),
+                         dtype={
+                            "seqname": pd.StringDtype(),
+                            "source": pd.StringDtype(),
+                            "feature": pd.StringDtype(),
+                            "start": pd.Int64Dtype(),
+                            "end": pd.Int64Dtype(),
+                            "score": pd.StringDtype(),
+                            "strand": pd.CategoricalDtype(categories=["+", "-"],
+                                                            ordered=False),
+                            "frame": pd.StringDtype(),
+                            "attribute": pd.StringDtype(),
+                          },
+                          comment='#'
+                        )
+    logger.info("Done reading %s. Found %d GTF entries ", par["gtf"], result.shape[0])
+    logger.info("GTF file is providing information for the following chromosomes: \n%s", 
+                fill(", ".join(result['seqname'].unique()), width=100))
+    logger.info("The following sources were specified in the GTF file:\n%s",
+                ", ".join(result["source"].unique()))
+    return result
+    
+
+def parse_attributes(attributes_series: pd.Series):
+    attribute_dict = dict()
+    attributes_list = [attr.strip().split(" ")
+                       for attr in attributes_series["attribute"].strip(";").split(";")]
+    for (attr_name, attr_value) in attributes_list:
+        attribute_dict.setdefault(attr_name, []).append(attr_value.strip('"'))
+    attribute_dict = {attr_name: "|".join(attr_value) 
+                      for attr_name, attr_value in attribute_dict.items()}
+    return pd.Series(attribute_dict)
+    
+
+def main(par):
+    logger.info(f"{meta['name']} started.")
+    parameters_str = [f'\t{param}: {param_val}\n' for param, param_val in par.items()]
+    logger.info("Parameters:\n%s", "".join(parameters_str).rstrip())
+    gtf_file = read_gtf(par["gtf"])
+    sources = set(source for source in gtf_file["source"].unique() if source != "ERCC")
+    specific_gtf = False
+    feature = "gene"
+    if len(sources) == 1 and (source := sources[0]) \
+        and (source == "refGene" or source == "ncbiRefSeq"):
+        feature = "transcript"
+        specific_gtf = True
+        logger.info("Found specific GTF from %s, forcing filtering on feature type %s", source, feature)
+    logger.info("Filtering GTF entries for feature type '%s'.", feature)
+    gtf_file = gtf_file[gtf_file["feature"] == feature]
+    logger.info("After filtering %d entries are left.", gtf_file.shape[0])
+    logger.info("Parsing the GTF attributes")
+    annotation = gtf_file[["attribute"]].apply(parse_attributes, result_type="expand", axis=1)
+    logger.info("Found the following attributes in the GTF:\n%s", ", ".join(annotation.columns))
+    annotation = pd.concat([gtf_file.drop(["attribute"], axis=1), annotation], axis=1)
+    if specific_gtf:
+       logger.info("Because the source of the GTF is either 'ncbiRefSeq' or 'refGene', which"
+                   "caused forced filtering based on %s, the duplicate genes still need to be dropped.",
+                   feature)
+       annotation = annotation.drop_duplicates(subset=("gene_id", "gene_name"), keep=False)
+       logger.info("After dropping duplicates, %d entries are left", annotation.shape[0])
+
+    # detect ensembl ids
+    # some GTF files contain version in ENSEMBL, e.g. ENS00000000046319.1
+    # we remove the version, because the annotation packages don't contain the version
+    if "gene_id" in annotation.columns:
+        logger.info("'gene_id' column was detected in attributes. Performing extra parsing of ENSEMBL ids.")
+        annotation["ENSEMBL_with_version"] = annotation["gene_id"].where(annotation["gene_id"].str.startswith("ENS"))
+        annotation["ENSEMBL"] = annotation["ENSEMBL_with_version"].str.replace(r"\.\d+$", "", regex=True)
+        annotation["gene_id"] = annotation["gene_id"].str.replace(r"\.\d+$", "", regex=True)
+
+    possible_name_columns = ("Name", "name", "gene_name")
+    found_columns = list(filter(lambda col_name: col_name in annotation, possible_name_columns))
+    # The following code allows to select a value for the SYMBOL column based on the first non-na column
+    if found_columns:
+        logger.info("Found one the following columns: %s; which can be used to populate the SYMBOL column",
+                    ", ".join(possible_name_columns))
+        # For each row (gtf entry), get the name of the first column that actually holds a value.
+        column_to_get = annotation.loc[:,found_columns].apply(pd.Series.first_valid_index, axis=1)
+        counts_per_column = column_to_get.value_counts(dropna=False).to_dict()
+        counts_per_column_str = [f'\t{col}: {counts}\n' for col, counts in counts_per_column.items()]
+        logger.info("Frequencies of the origin for the entries in the SYMBOL column:\n%s",
+                    "".join(counts_per_column_str).rstrip())
+        # If all columns hold NA for a certain row, first_valid_index will return None.
+        # Just use the name of the first column.
+        column_to_get = column_to_get.fillna(found_columns[0])
+        # We now have a list one column name per row, use it so select the values
+        # Loc cannot be used here because 1 value per row is required, 
+        # and loc will select for each row all the columns in columns_to_get
+        idx, cols = pd.factorize(column_to_get)
+        symbol_values = annotation.reindex(cols, axis=1).to_numpy()[np.arange(len(annotation)), idx]
+        annotation["SYMBOL"] = symbol_values
+    logger.info("Dropping unused columns")
+    annotation = annotation.drop(["score", "source", "frame", "feature"], axis=1)
+    logger.info("Looking for duplicate rows and removing them. Starting with %i entries", annotation.shape[0])
+    annotation = annotation.drop_duplicates(keep="first", ignore_index=True)
+    logger.info("After removing duplicates: %i entries", annotation.shape[0])
+    logger.info("Writing to %s", par["output"])
+    annotation.to_csv(par["output"], sep="\t", header=True, index=False, na_rep="NA")
+    # Do these checks *after* writing the csv in order to be able to check the data
+    logger.info("Checking for unique gene IDs")
+    if not annotation["gene_id"].is_unique:
+        raise ValueError("Values from the 'gene_id' column are not unique after processing!") 
+    logger.info("%s finished", meta['name'])
+
+
+if __name__ == "__main__":
+    main(par)
--- a/src/eset/create_fdata/test.py
+++ b/src/eset/create_fdata/test.py
@@ -0,0 +1,102 @@
+import pytest
+import sys
+import pandas as pd
+from pathlib import Path
+from uuid import uuid4
+from shutil import copyfile
+
+### VIASH START
+meta = {
+    "resources_dir": "./src/eset/create_fdata/",
+    "executable": "target/executable/eset/create_fdata/create_fdata",
+    "config": "src/eset/create_fdata/config.vsh.yaml"
+}
+### VIASH END
+
+@pytest.fixture
+def test_annotation_path():
+    return Path(meta["resources_dir"]) / "test_annotation.gtf"
+
+
+@pytest.fixture
+def random_path(tmp_path):
+    def wrapper(extension=None):
+        extension = "" if not extension else f".{extension}"
+        return tmp_path / f"{uuid4()}{extension}"
+    return wrapper 
+
+
+def test_create_fdata(run_component, test_annotation_path, random_path):
+    output_path = random_path("tsv")
+    run_component([
+        "--gtf", test_annotation_path,
+        "--output", output_path
+    ])
+    assert output_path.is_file()
+    result = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+
+    expected_dict = {
+        "seqname": ["20", "20", "20", "21"],
+        "start": ["87250", "142590", "157454", "297570"],
+        "end": ["97094", "145751", "159163", "300321"],
+        "strand": ["+", "+", "+", "+"],
+        "gene_id": ["ENSG00000178591", "ENSG00000125788",
+                    "ENSG00000088782", "ENSG00000247315"],
+        "gene_version": ["7", "6", "5", "4"],
+        "gene_name": ["DEFB125", "DEFB126", "DEFB127", pd.NA],
+        "gene_source": ["ensembl_havana", "ensembl_havana",
+                        "ensembl_havana", "havana"],
+        "gene_biotype": ["protein_coding", "protein_coding",
+                         "protein_coding", "protein_coding"],
+        "ENSEMBL_with_version": ["ENSG00000178591.7", "ENSG00000125788",
+                                 "ENSG00000088782", "ENSG00000247315"],
+        "ENSEMBL": ["ENSG00000178591", "ENSG00000125788",
+                    "ENSG00000088782", "ENSG00000247315"],
+        "SYMBOL": ["DEFB125", "DEFB126", "DEFB127", pd.NA]
+    }
+    expected = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(expected, result, check_like=True)
+
+
+def test_make_unique(run_component, test_annotation_path, random_path):
+    gtf_with_duplicate_entry_path = random_path("gtf")
+    output_path = random_path("tsv")
+    entry_to_add = (
+        "\n20	ensembl_havana	gene	87250	97094	.	+	.	gene_id " +
+        "\"ENSG00000178591.7\"; gene_version \"7\"; gene_name \"DEFB125\"; " +
+        "gene_source \"ensembl_havana\"; gene_biotype \"protein_coding\";\n"
+    )
+    copyfile(test_annotation_path, gtf_with_duplicate_entry_path)
+    with gtf_with_duplicate_entry_path.open("a") as open_gtf:
+        open_gtf.write(entry_to_add)
+    run_component([
+        "--gtf", gtf_with_duplicate_entry_path,
+        "--output", output_path
+    ])
+    assert output_path.is_file()
+    result = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+    expected_dict = {
+        "seqname": ["20", "20", "20", "21"],
+        "start": ["87250", "142590", "157454", "297570"],
+        "end": ["97094", "145751", "159163", "300321"],
+        "strand": ["+", "+", "+", "+"],
+        "gene_id": ["ENSG00000178591", "ENSG00000125788",
+                    "ENSG00000088782", "ENSG00000247315"],
+        "gene_version": ["7", "6", "5", "4"],
+        "gene_name": ["DEFB125", "DEFB126", "DEFB127", pd.NA],
+        "gene_source": ["ensembl_havana", "ensembl_havana",
+                        "ensembl_havana", "havana"],
+        "gene_biotype": ["protein_coding", "protein_coding",
+                         "protein_coding", "protein_coding"],
+        "ENSEMBL_with_version": ["ENSG00000178591.7", "ENSG00000125788",
+                                 "ENSG00000088782", "ENSG00000247315"],
+        "ENSEMBL": ["ENSG00000178591", "ENSG00000125788",
+                    "ENSG00000088782", "ENSG00000247315"],
+        "SYMBOL": ["DEFB125", "DEFB126", "DEFB127", pd.NA]
+    }
+    expected = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(expected, result, check_like=True)
+
+
+if __name__ == '__main__':
+    sys.exit(pytest.main([__file__]))
--- a/src/eset/create_fdata/test_annotation.gtf
+++ b/src/eset/create_fdata/test_annotation.gtf
@@ -0,0 +1,45 @@
+20	ensembl_havana	gene	87250	97094	.	+	.	gene_id "ENSG00000178591.7"; gene_version "7"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding";
+20	havana	transcript	87250	97094	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000608838"; transcript_version "1"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; transcript_support_level "2";
+20	havana	exon	87250	87359	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000608838"; transcript_version "1"; exon_number "1"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00003702629"; exon_version "1"; transcript_support_level "2";
+20	havana	exon	96005	97094	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000608838"; transcript_version "1"; exon_number "2"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00003705060"; exon_version "1"; transcript_support_level "2";
+20	ensembl_havana	transcript	87672	97094	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	exon	87672	87767	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; exon_number "1"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; exon_id "ENSE00001491993"; exon_version "2"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	CDS	87710	87767	.	+	0	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; exon_number "1"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; protein_id "ENSP00000371847"; protein_version "2"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	start_codon	87710	87712	.	+	0	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; exon_number "1"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	exon	96005	97094	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; exon_number "2"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; exon_id "ENSE00001491984"; exon_version "3"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	CDS	96005	96414	.	+	2	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; exon_number "2"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; protein_id "ENSP00000371847"; protein_version "2"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	stop_codon	96415	96417	.	+	0	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; exon_number "2"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	five_prime_utr	87672	87709	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	three_prime_utr	96418	97094	.	+	.	gene_id "ENSG00000178591"; gene_version "7"; transcript_id "ENST00000382410"; transcript_version "3"; gene_name "DEFB125"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB125-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12989"; tag "basic"; transcript_support_level "1 (assigned to previous version 2)";
+20	ensembl_havana	gene	142590	145751	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding";
+20	ensembl_havana	transcript	142590	145751	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	exon	142590	142686	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; exon_number "1"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; exon_id "ENSE00001491976"; exon_version "4"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	CDS	142629	142686	.	+	0	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; exon_number "1"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; protein_id "ENSP00000371835"; protein_version "3"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	start_codon	142629	142631	.	+	0	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; exon_number "1"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	exon	145415	145751	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; exon_number "2"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; exon_id "ENSE00000858522"; exon_version "4"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	CDS	145415	145689	.	+	2	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; exon_number "2"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; protein_id "ENSP00000371835"; protein_version "3"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	stop_codon	145690	145692	.	+	0	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; exon_number "2"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	five_prime_utr	142590	142628	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	three_prime_utr	145693	145751	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000382398"; transcript_version "4"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12990"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	havana	transcript	142634	145749	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000542572"; transcript_version "1"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; tag "mRNA_start_NF"; transcript_support_level "3";
+20	havana	exon	142634	142686	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000542572"; transcript_version "1"; exon_number "1"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00002285856"; exon_version "1"; tag "mRNA_start_NF"; transcript_support_level "3";
+20	havana	exon	145415	145488	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000542572"; transcript_version "1"; exon_number "2"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00002303512"; exon_version "1"; tag "mRNA_start_NF"; transcript_support_level "3";
+20	havana	exon	145579	145749	.	+	.	gene_id "ENSG00000125788"; gene_version "6"; transcript_id "ENST00000542572"; transcript_version "1"; exon_number "3"; gene_name "DEFB126"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB126-202"; transcript_source "havana"; transcript_biotype "processed_transcript"; exon_id "ENSE00002217818"; exon_version "1"; tag "mRNA_start_NF"; transcript_support_level "3";
+20	ensembl_havana	gene	157454	159163	.	+	.	gene_id "ENSG00000088782"; gene_version "5"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding";
+20	ensembl_havana	transcript	157454	159163	.	+	.	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	exon	157454	157593	.	+	.	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; exon_number "1"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; exon_id "ENSE00001491947"; exon_version "4"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	CDS	157545	157593	.	+	0	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; exon_number "1"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; protein_id "ENSP00000371825"; protein_version "3"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	start_codon	157545	157547	.	+	0	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; exon_number "1"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	exon	158774	159163	.	+	.	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; exon_number "2"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; exon_id "ENSE00001166560"; exon_version "3"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	CDS	158774	159021	.	+	2	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; exon_number "2"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; protein_id "ENSP00000371825"; protein_version "3"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	stop_codon	159022	159024	.	+	0	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; exon_number "2"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	five_prime_utr	157454	157544	.	+	.	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+20	ensembl_havana	three_prime_utr	159025	159163	.	+	.	gene_id "ENSG00000088782"; gene_version "5"; transcript_id "ENST00000382388"; transcript_version "4"; gene_name "DEFB127"; gene_source "ensembl_havana"; gene_biotype "protein_coding"; transcript_name "DEFB127-201"; transcript_source "ensembl_havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS12991"; tag "basic"; transcript_support_level "1 (assigned to previous version 3)";
+21	havana	gene	297570	300321	.	+	.	gene_id "ENSG00000247315"; gene_version "4"; gene_source "havana"; gene_biotype "protein_coding";
+21	havana	transcript	297570	300321	.	+	.	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
+21	havana	exon	297570	300321	.	+	.	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; exon_number "1"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; exon_id "ENSE00001977652"; exon_version "4"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
+21	havana	CDS	297587	298795	.	+	0	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; exon_number "1"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; protein_id "ENSP00000484056"; protein_version "1"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
+21	havana	start_codon	297587	297589	.	+	0	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; exon_number "1"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
+21	havana	stop_codon	298796	298798	.	+	0	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; exon_number "1"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
+21	havana	five_prime_utr	297570	297586	.	+	.	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
+21	havana	three_prime_utr	298799	300321	.	+	.	gene_id "ENSG00000247315"; gene_version "4"; transcript_id "ENST00000500893"; transcript_version "4"; gene_source "havana"; gene_biotype "protein_coding"; transcript_name "ZCCHC3-201"; transcript_source "havana"; transcript_biotype "protein_coding"; tag "CCDS"; ccds_id "CCDS42844"; tag "basic"; transcript_support_level "NA (assigned to previous version 3)";
--- a/src/eset/create_pdata/config.vsh.yaml
+++ b/src/eset/create_pdata/config.vsh.yaml
@@ -0,0 +1,55 @@
+name: create_pdata
+namespace: eset
+description: |
+  Create a pdata file by combining the mapping statistics 
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ maintainer ]
+  - __merge__: /src/base/authors/marijke_van_moerbeke.yaml
+    roles: [ contributor ]
+arguments:
+- name: "--star_stats_file"
+  type: file
+  description: |
+    Tab-delimited text file containing statistics (per column) that were generated
+    from the STAR log files (Log.final.out, Summary.csv, ReadsPerGene.out.tab).
+    Each entry (row) in the file describes the values for one well (barcode).
+  required: true
+- name: "--nrReadsNrGenesPerChromPool"
+  type: file
+  description: |
+    Pivot table in tsv format of the combined nrReadsNrGenesPerChrom files from STAR. 
+    Describes per chromosome (as columns) the number of reads, as well as the total number 
+    of reads per cell barcode and the percentage of nuclear, ERCC and mitochondrial
+    reads.
+  required: true
+- name: "--output"
+  type: file
+  direction: output
+  default: pData.$id.txt
+resources:
+- type: python_script
+  path: create_pdata.py
+test_resources:
+- type: python_script
+  path: test.py
+- path: nrReadsNrGenesPerChromPool.txt
+- path: starLogs.txt
+
+engines:
+  - type: docker
+    image: python:3.12-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - pandas
+    test_setup:
+      - type: python
+        packages:
+          - viashpy
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/eset/create_pdata/create_pdata.py
+++ b/src/eset/create_pdata/create_pdata.py
@@ -0,0 +1,63 @@
+from itertools import batched
+import pandas as pd
+import logging
+
+### VIASH START
+meta = {
+    "name": "create_pdata",
+}
+
+par = {
+  "star_stats_file": "src/eset/create_pdata/starLogs.txt",
+  "nrReadsNrGenesPerChromPool": "src/eset/create_pdata/nrReadsNrGenesPerChromPool.txt",
+  "output": "pData.tsv"
+}
+
+### VIASH END
+
+logger = logging.getLogger()
+console_handler = logging.StreamHandler()
+logger.addHandler(console_handler)
+logger.setLevel(logging.DEBUG)
+
+def main(par):
+  logger.info(f"{meta['name']} started.")
+  parameters_str = [f'\t{param}: {param_val}\n' for param, param_val in par.items()]
+  logger.info("Parameters:\n%s", "".join(parameters_str).rstrip())
+  logger.info("Reading %s", par["star_stats_file"])
+  star_log_stats = pd.read_csv(par["star_stats_file"], sep="\t", index_col=0)
+  logger.info("STAR log statics file contains information for the following barcodes: %s", 
+              ", ".join(star_log_stats.index))
+  logger.info("Reading %s", par["nrReadsNrGenesPerChromPool"])
+  reads_and_genes_per_chr_stats = pd.read_csv(par["nrReadsNrGenesPerChromPool"], sep="\t", index_col=0)
+  logger.info("Reads per gene and chromosome table contains information for the following barcodes: %s",
+              ", ".join(reads_and_genes_per_chr_stats.index))
+  logger.info("Filtering mapping statistics file columns.")
+  cols_to_keep = ("WellID", "NumberOfMTReads", "pctMT", "NumberOfERCCReads",
+                  "pctERCC", "NumberOfChromReads", "pctChrom")
+  try:
+    reads_and_genes_per_chr_stats = reads_and_genes_per_chr_stats.loc[:,cols_to_keep]
+  except KeyError as e:
+    raise KeyError("When trying to subset the reads per genes and chromosomes file, "
+                   "a column was missing. Available columns in the file: "
+                   f"{', '.join(reads_and_genes_per_chr_stats.columns)}.") from e
+  # Each barcode should be present. An alternative approach could be to just
+  # do the concatenation and check for NA values that are filled for non-overlapping
+  # index values, but there are already NA values present in the dataframes
+  if not star_log_stats.index.sort_values().equals(reads_and_genes_per_chr_stats.index.sort_values()):
+    raise ValueError("Error while combining two log files. It seems that the entries (barcodes) "
+                     f"do not fully overlap. Barcodes in '{par['star_stats_file']}: "
+                     f"{', '.join(reads_and_genes_per_chr_stats.index)}. Barcodes in "
+                     f"'{par['nrReadsNrGenesPerChromPool']}': "
+                     f"{', '.join(star_log_stats.index)}")
+  combined_stats = pd.concat([reads_and_genes_per_chr_stats, star_log_stats], axis=1)
+  logger.info("Summary of final output:\n%s\n",
+                "\n".join(repr(combined_stats.loc[:,columns].describe())
+                          for columns in batched(combined_stats.columns, 3))) 
+  logger.info("Writing to %s", par["output"])
+  combined_stats.reset_index("WellBC").to_csv(par["output"], sep="\t", header=True, index=False)
+  logger.info("Finished %s.", meta["name"])
+
+
+if __name__ == "__main__":
+  main(par)
--- a/src/eset/create_pdata/nrReadsNrGenesPerChromPool.txt
+++ b/src/eset/create_pdata/nrReadsNrGenesPerChromPool.txt
@@ -0,0 +1,8 @@
+WellBC	WellID	20	pctChrom	pctMT	pctERCC	SumReads	NumberOfGenes	NumberOfERCCReads	NumberOfChromReads	NumberOfMTReads
+AACAAGGTAC	A1	8542	100	0	0	8542	408	0	8542	0
+ACGCCTTCGT	A2	5863	100	0	0	5863	377	0	5863	0
+CCATACTGAC	A3	7396	100	0	0	7396	391	0	7396	0
+GCAAGCGAAT	B1	10092	100	0	0	10092	420	0	10092	0
+GTCTCGAGTG	C5	470	100	0	0	470	150	0	470	0
+TGCGCTCATT	D6	7650	100	0	0	7650	407	0	7650	0
+TTGTGTTCGA	E19	9422	100	0	0	9422	420	0	9422	0
--- a/src/eset/create_pdata/starLogs.txt
+++ b/src/eset/create_pdata/starLogs.txt
@@ -0,0 +1,8 @@
+WellBC	NumberOfInputReads	NumberOfMappedReads	PctMappedReads	NumberOfReadsMappedToMultipleLoci	PectOfReadsMappedToMultipleLoci	NumberOfReadsMappedToTooManyLoci	PectOfReadsMappedToTooManyLoci	NumberOfReadsUnmappedTooManyMismatches	PectOfReadsUnmappedTooManyMismatches	NumberOfReadsUnmappedTooShort	PectOfReadsUnmappedTooShort	NumberOfReadsUnmappedOther	PectOfReadsUnmappedOther	ReadsWithValidBarcodes	SequencingSaturation	Q30BasesInCB+UMI	ReadsMappedToTranscriptome:Unique+MultipeGenes	EstimatedNumberOfCells	FractionOfReadsInCells	MeanReadsPerCell	NumberOfUMIs	NumberOfGenes	NumberOfCountedReads
+ACGCCTTCGT	96430	16869	17.49	0	0	6124	6.35	0	0	73375	76.09	62	0.06	0.999782	0.0665302	0.980077	0.0620969	1	1	5862	5472	377	6463
+GTCTCGAGTG	10158	1902	18.72	0	0	967	9.52	0	0	7280	71.67	9	0.09	0.999803	0.0553191	0.984451	0.0476472	1	1	470	444	150	533
+GCAAGCGAAT	156134	24005	15.37	0	0	7961	5.1	0	0	124096	79.48	72	0.05	0.999744	0.0680872	0.982779	0.0658665	1	1	10090	9403	420	11273
+CCATACTGAC	113577	17319	15.25	0	0	5905	5.2	0	0	90292	79.5	61	0.05	0.999859	0.0717282	0.982313	0.066554	1	1	7389	6859	391	8299
+TGCGCTCATT	126989	19272	15.18	0	0	7141	5.62	0	0	100515	79.15	61	0.05	0.999843	0.0667974	0.986581	0.0616668	1	1	7650	7139	407	8444
+TTGTGTTCGA	142560	22129	15.52	0	0	7045	4.94	0	0	113324	79.49	62	0.04	0.999783	0.060828	0.986622	0.0676838	1	1	9420	8847	420	10383
+AACAAGGTAC	141303	23749	16.81	0	0	8458	5.99	0	0	109035	77.16	61	0.04	0.999816	0.0698056	0.979965	0.0618175	1	1	8538	7942	408	9535
--- a/src/eset/create_pdata/test.py
+++ b/src/eset/create_pdata/test.py
@@ -0,0 +1,173 @@
+import pytest
+import sys
+import pandas as pd
+from pathlib import Path
+from uuid import uuid4
+
+### VIASH START
+meta = {
+    "resources_dir": "./src/eset/create_pdata/",
+    "executable": "target/executable/eset/create_pdata/create_pdata",
+    "config": "src/eset/create_pdata/config.vsh.yaml"
+}
+### VIASH END
+
+@pytest.fixture
+def test_reads_and_genes_per_chr_path():
+    return Path(meta["resources_dir"]) / "nrReadsNrGenesPerChromPool.txt"
+
+
+@pytest.fixture
+def test_star_logs_summary_path():
+    return Path(meta["resources_dir"]) / "starLogs.txt"
+
+
+@pytest.fixture
+def random_path(tmp_path):
+    def wrapper(extension=None):
+        extension = "" if not extension else f".{extension}"
+        return tmp_path / f"{uuid4()}{extension}"
+    return wrapper 
+
+
+def test_create_pdata(run_component, test_reads_and_genes_per_chr_path,
+                      test_star_logs_summary_path, random_path):
+    output_path = random_path("tsv")
+    run_component([
+        "--star_stats_file", test_star_logs_summary_path,
+        "--nrReadsNrGenesPerChromPool", test_reads_and_genes_per_chr_path, 
+        "--output", output_path
+    ])
+    assert output_path.is_file()
+    result = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+    expected_dict = {
+        'WellBC': ['AACAAGGTAC', 'ACGCCTTCGT', 'CCATACTGAC', 'GCAAGCGAAT',
+                   'GTCTCGAGTG', 'TGCGCTCATT', 'TTGTGTTCGA'],
+        'WellID': ['A1', 'A2', 'A3', 'B1', 'C5', 'D6', 'E19'],
+        'NumberOfMTReads': ['0', '0', '0', '0', '0', '0', '0'],
+        'pctMT': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfERCCReads': ['0', '0', '0', '0', '0', '0', '0'],
+        'pctERCC': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfChromReads': ['8542', '5863', '7396', '10092', '470',
+                               '7650', '9422'],
+        'pctChrom': ['100', '100', '100', '100', '100', '100', '100'],
+        'NumberOfInputReads': ['141303', '96430', '113577', '156134', '10158',
+                               '126989', '142560'],
+        'NumberOfMappedReads': ['23749', '16869', '17319', '24005', '1902',
+                                '19272', '22129'],
+        'PctMappedReads': ['16.81', '17.49', '15.25', '15.37', '18.72',
+                           '15.18', '15.52'],
+        'NumberOfReadsMappedToMultipleLoci': ['0', '0', '0', '0', '0', '0', '0'],
+        'PectOfReadsMappedToMultipleLoci': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfReadsMappedToTooManyLoci': ['8458', '6124', '5905', '7961', '967',
+                                             '7141', '7045'],
+        'PectOfReadsMappedToTooManyLoci': ['5.99', '6.35', '5.2', '5.1', '9.52',
+                                           '5.62', '4.94'],
+        'NumberOfReadsUnmappedTooManyMismatches': ['0', '0', '0', '0', '0', '0', '0'],
+        'PectOfReadsUnmappedTooManyMismatches': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfReadsUnmappedTooShort': ['109035', '73375', '90292', '124096',
+                                          '7280', '100515', '113324'],
+        'PectOfReadsUnmappedTooShort': ['77.16', '76.09', '79.5', '79.48',
+                                        '71.67', '79.15', '79.49'],
+        'NumberOfReadsUnmappedOther': ['61', '62', '61', '72', '9', '61', '62'],
+        'PectOfReadsUnmappedOther': ['0.04', '0.06', '0.05', '0.05',
+                                     '0.09', '0.05', '0.04'],
+        'ReadsWithValidBarcodes': ['0.999816', '0.999782', '0.999859', '0.999744',
+                                   '0.999803', '0.999843', '0.999783'],
+        'SequencingSaturation': ['0.0698056', '0.0665302', '0.0717282', '0.0680872',
+                                 '0.0553191', '0.0667974', '0.060828'],
+        'Q30BasesInCB+UMI': ['0.979965', '0.980077', '0.982313', '0.982779',
+                             '0.984451', '0.986581', '0.986622'],
+        'ReadsMappedToTranscriptome:Unique+MultipeGenes': ['0.0618175', '0.0620969',
+                                                           '0.066554', '0.0658665',
+                                                           '0.0476472', '0.0616668',
+                                                           '0.0676838'],
+        'EstimatedNumberOfCells': ['1', '1', '1', '1', '1', '1', '1'],
+        'FractionOfReadsInCells': ['1', '1', '1', '1', '1', '1', '1'],
+        'MeanReadsPerCell': ['8538', '5862', '7389',
+                             '10090', '470', '7650', '9420'],
+        'NumberOfUMIs': ['7942', '5472', '6859', '9403',
+                         '444', '7139', '8847'],
+        'NumberOfGenes': ['408', '377', '391', '420', '150', '407', '420'],
+        'NumberOfCountedReads': ['9535', '6463', '8299', '11273',
+                                 '533', '8444', '10383']
+    }
+    expected = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(result, expected, check_like=True)
+
+def test_na(run_component, test_reads_and_genes_per_chr_path,
+                    test_star_logs_summary_path, random_path):
+    """
+    The star log summary can contain NA values.
+    """
+    output_path = random_path("tsv")
+    summary_with_na_path = random_path("txt")
+    original_summary = pd.read_csv(test_star_logs_summary_path,
+                                   sep="\t", index_col=0)
+    original_summary.loc["GTCTCGAGTG", "FractionOfReadsInCells"] = pd.NA
+    original_summary.reset_index("WellBC").to_csv(summary_with_na_path, sep="\t",
+                                                  header=True, index=False)
+    run_component([
+        "--star_stats_file", summary_with_na_path,
+        "--nrReadsNrGenesPerChromPool", test_reads_and_genes_per_chr_path,
+        "--output", output_path
+    ])
+    expected_dict = {
+        'WellBC': ['AACAAGGTAC', 'ACGCCTTCGT', 'CCATACTGAC', 'GCAAGCGAAT',
+                   'GTCTCGAGTG', 'TGCGCTCATT', 'TTGTGTTCGA'],
+        'WellID': ['A1', 'A2', 'A3', 'B1', 'C5', 'D6', 'E19'],
+        'NumberOfMTReads': ['0', '0', '0', '0', '0', '0', '0'],
+        'pctMT': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfERCCReads': ['0', '0', '0', '0', '0', '0', '0'],
+        'pctERCC': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfChromReads': ['8542', '5863', '7396', '10092', '470',
+                               '7650', '9422'],
+        'pctChrom': ['100', '100', '100', '100', '100', '100', '100'],
+        'NumberOfInputReads': ['141303', '96430', '113577', '156134', '10158',
+                               '126989', '142560'],
+        'NumberOfMappedReads': ['23749', '16869', '17319', '24005', '1902',
+                                '19272', '22129'],
+        'PctMappedReads': ['16.81', '17.49', '15.25', '15.37', '18.72',
+                           '15.18', '15.52'],
+        'NumberOfReadsMappedToMultipleLoci': ['0', '0', '0', '0', '0', '0', '0'],
+        'PectOfReadsMappedToMultipleLoci': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfReadsMappedToTooManyLoci': ['8458', '6124', '5905', '7961', '967',
+                                             '7141', '7045'],
+        'PectOfReadsMappedToTooManyLoci': ['5.99', '6.35', '5.2', '5.1', '9.52',
+                                           '5.62', '4.94'],
+        'NumberOfReadsUnmappedTooManyMismatches': ['0', '0', '0', '0', '0', '0', '0'],
+        'PectOfReadsUnmappedTooManyMismatches': ['0', '0', '0', '0', '0', '0', '0'],
+        'NumberOfReadsUnmappedTooShort': ['109035', '73375', '90292', '124096',
+                                          '7280', '100515', '113324'],
+        'PectOfReadsUnmappedTooShort': ['77.16', '76.09', '79.5', '79.48',
+                                        '71.67', '79.15', '79.49'],
+        'NumberOfReadsUnmappedOther': ['61', '62', '61', '72', '9', '61', '62'],
+        'PectOfReadsUnmappedOther': ['0.04', '0.06', '0.05', '0.05',
+                                     '0.09', '0.05', '0.04'],
+        'ReadsWithValidBarcodes': ['0.999816', '0.999782', '0.999859', '0.999744',
+                                   '0.999803', '0.999843', '0.999783'],
+        'SequencingSaturation': ['0.0698056', '0.0665302', '0.0717282', '0.0680872',
+                                 '0.0553191', '0.0667974', '0.060828'],
+        'Q30BasesInCB+UMI': ['0.979965', '0.980077', '0.982313', '0.982779',
+                             '0.984451', '0.986581', '0.986622'],
+        'ReadsMappedToTranscriptome:Unique+MultipeGenes': ['0.0618175', '0.0620969',
+                                                           '0.066554', '0.0658665',
+                                                           '0.0476472', '0.0616668',
+                                                           '0.0676838'],
+        'EstimatedNumberOfCells': ['1', '1', '1', '1', '1', '1', '1'],
+        'FractionOfReadsInCells': ['1.0', '1.0', '1.0', '1.0', pd.NA, '1.0', '1.0'],
+        'MeanReadsPerCell': ['8538', '5862', '7389',
+                             '10090', '470', '7650', '9420'],
+        'NumberOfUMIs': ['7942', '5472', '6859', '9403',
+                         '444', '7139', '8847'],
+        'NumberOfGenes': ['408', '377', '391', '420', '150', '407', '420'],
+        'NumberOfCountedReads': ['9535', '6463', '8299', '11273',
+                                 '533', '8444', '10383']
+    }
+    result = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+    expected = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(result, expected, check_like=True)
+
+
+if __name__ == '__main__':
+    sys.exit(pytest.main([__file__]))
--- a/src/integration_test_components/htrnaseq/check_eset/config.vsh.yaml
+++ b/src/integration_test_components/htrnaseq/check_eset/config.vsh.yaml
@@ -0,0 +1,34 @@
+name: "check_eset"
+namespace: "integration_test_components/htrnaseq"
+description: "This component test the ExpressionSet object as output by the main pipeline."
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ author, maintainer ]
+argument_groups:
+  - name: Inputs
+    arguments:
+      - name: "--eset"
+        type: file
+        required: true
+        description: Path to an ExpressionSet object.
+        example: eset.rds
+      - name: "--star_output"
+        type: file
+        required: true
+        multiple: true
+resources:
+  - type: r_script
+    path: script.R
+engines:
+  - type: docker
+    image: bioconductor/bioconductor_docker:3.19
+    setup:
+      - type: r
+        cran:
+          - bit64
+        bioc:
+          - Biobase
+
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/integration_test_components/htrnaseq/check_eset/script.R
+++ b/src/integration_test_components/htrnaseq/check_eset/script.R
@@ -0,0 +1,198 @@
+library(Biobase)
+library(testthat)
+library(Matrix)
+
+sample_1_result <- readRDS(par$eset)
+expected_sample_names <- c(
+  "sample_one_AACAAGGTAC", "sample_one_AACAATCAGG", "sample_one_AACACCTAGT",
+  "sample_one_AACAGGCAAT", "sample_one_AACATGGAGA", "sample_one_AACATTACCG",
+  "sample_one_AACCAGCCAG", "sample_one_AACCAGTTGA", "sample_one_AACCGCGACT",
+  "sample_one_AACCGGAAGG", "sample_one_AACCGGCGTA", "sample_one_AACCTAGTCC",
+  "sample_one_AACCTCATAG", "sample_one_AACGTAAGCT", "sample_one_AACTCTACAC",
+  "sample_one_AACTGTGTCA", "sample_one_AAGACGGATT", "sample_one_AAGATCGGCG",
+  "sample_one_AAGATGTCCA", "sample_one_AAGCATATGG", "sample_one_AAGCGATGTT",
+  "sample_one_AAGCGTTCAG", "sample_one_AAGCTCACCT", "sample_one_AAGGCATGCG",
+  "sample_one_AAGGTCTGGA", "sample_one_AAGTTAGCGC", "sample_one_AAGTTCCTTG",
+  "sample_one_AATACCGGTA", "sample_one_AATAGCCACA", "sample_one_AATCACGCGA",
+  "sample_one_AATCCATCTG", "sample_one_AATCCGCTCC", "sample_one_AATCCTACCA",
+  "sample_one_AATCGTCCGC", "sample_one_AATGAACACG", "sample_one_AATGACCTTC",
+  "sample_one_AATGAGAGCA", "sample_one_AATGTCAGTG", "sample_one_AATTAGGCCG",
+  "sample_one_AATTGCGATG", "sample_one_ACAACAGTCG", "sample_one_ACAACCATAC",
+  "sample_one_ACAACGGAGC", "sample_one_ACAAGCGCGA", "sample_one_ACACAATCTC",
+  "sample_one_ACACAGTGAA", "sample_one_ACACCGAATT", "sample_one_ACACGCAGTA",
+  "sample_one_ACACGGTCCT", "sample_one_ACACTTGCTG", "sample_one_ACAGTGCCAA",
+  "sample_one_ACATGTGTGC", "sample_one_ACCAGGACCA", "sample_one_ACCATAACAC",
+  "sample_one_ACCGAACCGT", "sample_one_ACCGAGAGTC", "sample_one_ACCGGTACAG",
+  "sample_one_ACCGTACTTC", "sample_one_ACCTCCGACA", "sample_one_ACCTCTCTCC",
+  "sample_one_ACCTGTCCGA", "sample_one_ACCTTATGTG", "sample_one_ACGAATGACA",
+  "sample_one_ACGCCTCAAC", "sample_one_ACGCCTTCGT", "sample_one_ACGCTGGATA",
+  "sample_one_ACGGTCCGTT", "sample_one_ACGTAGGCAC", "sample_one_ACGTGCTGAT",
+  "sample_one_ACTCCAAGCC", "sample_one_ACTGGCGCAT", "sample_one_ACTGGCTTCC",
+  "sample_one_ACTTAACTGC", "sample_one_ACTTCATCAC", "sample_one_ACTTCGTTGA",
+  "sample_one_ACTTCTCCTG", "sample_one_ACTTGAGGAA", "sample_one_ACTTGTAAGG",
+  "sample_one_AGAACCACGG", "sample_one_AGAAGCAATC", "sample_one_AGACCGTTAT",
+  "sample_one_AGACTAGCAT", "sample_one_AGAGATGCAG", "sample_one_AGAGCTTACA",
+  "sample_one_AGAGTGTAAC", "sample_one_AGAGTTCTGC", "sample_one_AGATAGTGCT",
+  "sample_one_AGCAATGCGC", "sample_one_AGCATGTCAT", "sample_one_AGCCACTAGC",
+  "sample_one_AGCCAGAATA", "sample_one_AGCCAGCTCT", "sample_one_AGCGATAACG",
+  "sample_one_AGCGTACAAT", "sample_one_AGCTATTCCA", "sample_one_AGCTCCTCAG",
+  "sample_one_AGGAGGCATA", "sample_one_AGGCGTCTGT", "sample_one_AGTAACTCAC",
+  "sample_one_AGTAAGCGTT", "sample_one_AGTCTGTACG", "sample_one_AGTGCAATGT",
+  "sample_one_ATAAGGTGCA", "sample_one_ATACACGACA", "sample_one_ATAGGCCATT",
+  "sample_one_ATATCCGCAT", "sample_one_ATCAGCACTT", "sample_one_ATCAGCGAGG",
+  "sample_one_ATCCAATACG", "sample_one_ATCCGCTGTG", "sample_one_ATCCGTCCAT",
+  "sample_one_ATCGACGGCT", "sample_one_ATCGCGATTA", "sample_one_ATCGGTAGGC",
+  "sample_one_ATCTAAGGAG", "sample_one_ATGACGGTAA", "sample_one_ATGACTCAGT",
+  "sample_one_ATGCACCGGA", "sample_one_ATGCGGACTG", "sample_one_ATGCTTCCTA",
+  "sample_one_ATGGACCAAC", "sample_one_ATGGTCTTAG", "sample_one_ATGGTGAGCG",
+  "sample_one_ATGTGGAAGC", "sample_one_ATTATCGGAC", "sample_one_ATTCGGAACA",
+  "sample_one_CAACAATCCA", "sample_one_CAAGAAGCAT", "sample_one_CAAGATGAGG",
+  "sample_one_CAAGCCAACG", "sample_one_CAAGTGGATC", "sample_one_CACAGTTCAT",
+  "sample_one_CACGAGTCTG", "sample_one_CACGCTCCAA", "sample_one_CACTGAGCAC",
+  "sample_one_CAGATCAATG", "sample_one_CAGTGCTCTT", "sample_one_CAGTTAAGCA",
+  "sample_one_CATAGCTATC", "sample_one_CATCACCACC", "sample_one_CATGTACGCC",
+  "sample_one_CATTACACTG", "sample_one_CATTCGACGA", "sample_one_CCAACTATGG",
+  "sample_one_CCAAGGAGTT", "sample_one_CCAATTGTTC", "sample_one_CCACAAGTGC",
+  "sample_one_CCAGCTTAGT", "sample_one_CCATAACTTG", "sample_one_CCATACTGAC",
+  "sample_one_CCATAGATCA", "sample_one_CCATGTGCTT", "sample_one_CCATTCAGCG",
+  "sample_one_CCGAACAAGC", "sample_one_CCGAACCTAA", "sample_one_CCGAAGACCT",
+  "sample_one_CCGAATAGTG", "sample_one_CCGACTTCTC", "sample_one_CCGATCCACT",
+  "sample_one_CCGATGATAC", "sample_one_CCGCGTTATG", "sample_one_CCGCTAGCTT",
+  "sample_one_CCGGAGTATC", "sample_one_CCGGCCAATT", "sample_one_CCGGTCTCTA",
+  "sample_one_CCGTACGATG", "sample_one_CCGTCAGAAC", "sample_one_CCTAGACACG",
+  "sample_one_CCTAGTTGAG", "sample_one_CCTATTCTGT", "sample_one_CCTCAACCGA",
+  "sample_one_CCTCCATAAG", "sample_one_CCTGATGCCA", "sample_one_CCTGCAATAC",
+  "sample_one_CCTTGTATTC", "sample_one_CGAGATCTCT", "sample_one_CGAGGAACAA",
+  "sample_one_CGATAACCGC", "sample_one_CGATCCTGTG", "sample_one_CGCCAACCAT",
+  "sample_one_CGCCAGTGTT", "sample_one_CGCCTTGTAC", "sample_one_CGCGGATTCA",
+  "sample_one_CGCTTAAGGC", "sample_one_CGCTTACTAA", "sample_one_CGCTTCTTGG",
+  "sample_one_CGGAAGCTGT", "sample_one_CGGAATACAC", "sample_one_CGGAGATTGG",
+  "sample_one_CGGAGCTCAA", "sample_one_CGGATCGGTA", "sample_one_CGGATTCTAG",
+  "sample_one_CGGCAACTTA", "sample_one_CGGCTCATCA", "sample_one_CGGTCGTATT",
+  "sample_one_CGGTGACATC", "sample_one_CGTAACGGAT", "sample_one_CGTAAGATTC",
+  "sample_one_CGTACTGTAA", "sample_one_CGTAGAAGAC", "sample_one_CGTCCTAGGA",
+  "sample_one_CGTCGGCAAT", "sample_one_CGTGAGTTAT", "sample_one_CGTGTCAAGC",
+  "sample_one_CTAACTTCAG", "sample_one_CTAATAGCGT", "sample_one_CTACACCAGG",
+  "sample_one_CTAGCACAAT", "sample_one_CTATGAACGG", "sample_one_CTCAAGGACC",
+  "sample_one_CTCACCTGTC", "sample_one_CTCCTATTGT", "sample_one_CTCGCAACGT",
+  "sample_one_CTCGTGCCTA", "sample_one_CTGGATTGAC", "sample_one_CTGTAGTCAG",
+  "sample_one_CTGTCGCTTC", "sample_one_CTGTCTGTGT", "sample_one_CTTCATATCG",
+  "sample_one_CTTGCTGACG", "sample_one_GAAGGATTAG", "sample_one_GAATCGAGCC",
+  "sample_one_GACCATCTAA", "sample_one_GACGACCACA", "sample_one_GAGACATCTT",
+  "sample_one_GAGCGAGTCA", "sample_one_GAGTAGACCA", "sample_one_GATACGCTTA",
+  "sample_one_GATAGACTGT", "sample_one_GATAGAGGCG", "sample_one_GATAGGTCAA",
+  "sample_one_GATATCAGGA", "sample_one_GATCTCATTC", "sample_one_GATCTGGTCG",
+  "sample_one_GATGAGTGAC", "sample_one_GATGGATACA", "sample_one_GATGTGACAG",
+  "sample_one_GATTAAGTCC", "sample_one_GATTGCACGC", "sample_one_GCAAGCGAAT",
+  "sample_one_GCAATGTAAG", "sample_one_GCACACTATA", "sample_one_GCACTCGGAA",
+  "sample_one_GCACTGCGTT", "sample_one_GCACTTAATC", "sample_one_GCAGGAGATG",
+  "sample_one_GCAGTACTGG", "sample_one_GCATATGAGT", "sample_one_GCATCCGATC",
+  "sample_one_GCCAAGTACA", "sample_one_GCCACGATTC", "sample_one_GCCATAGGTT",
+  "sample_one_GCCATATCGA", "sample_one_GCCGTCAATA", "sample_one_GCCTGGACAT",
+  "sample_one_GCGTAATTAC", "sample_one_GCTATTATCC", "sample_one_GCTCAGTAAT",
+  "sample_one_GCTGCTTATA", "sample_one_GGAATAAGCA", "sample_one_GGACGATGCT",
+  "sample_one_GGCATCGTGA", "sample_one_GGCATTATTG", "sample_one_GGCCGAGATT",
+  "sample_one_GGCGCTATAA", "sample_one_GGCGTTAAGT", "sample_one_GGCTATTGAT",
+  "sample_one_GGCTGCTACT", "sample_one_GGTAATGTGT", "sample_one_GGTGGTTGGA",
+  "sample_one_GGTGTTCACC", "sample_one_GGTTAGATCT", "sample_one_GGTTATGGCG",
+  "sample_one_GGTTCACTGG", "sample_one_GGTTGTGCAA", "sample_one_GTAACCAGTA",
+  "sample_one_GTAACCTTGG", "sample_one_GTAAGAACCT", "sample_one_GTAAGGCTCC",
+  "sample_one_GTAATCCACG", "sample_one_GTATTGTGGA", "sample_one_GTCCGCATCA",
+  "sample_one_GTCCTTCGGT", "sample_one_GTCGCTCTCT", "sample_one_GTCGGTGACA",
+  "sample_one_GTCTCGAGTG", "sample_one_GTCTCTTAAG", "sample_one_GTCTTCCGAG",
+  "sample_one_GTGACTATAC", "sample_one_GTGGTTAATG", "sample_one_GTGTGCCTGT",
+  "sample_one_GTGTGTGTCC", "sample_one_GTTCATTGCC", "sample_one_GTTCCGGTGA",
+  "sample_one_GTTCGTCGAA", "sample_one_GTTGAATTGG", "sample_one_GTTGATCCGC",
+  "sample_one_GTTGTATGCT", "sample_one_TAACCGTAGC", "sample_one_TAACGTCGAT",
+  "sample_one_TAAGGTACGG", "sample_one_TACGGACATA", "sample_one_TACTACCGCC",
+  "sample_one_TACTGTCAAG", "sample_one_TAGCGAACGC", "sample_one_TAGCGCCAAC",
+  "sample_one_TAGGACGCCT", "sample_one_TAGGTTGCAA", "sample_one_TAGTAGTCTC",
+  "sample_one_TAGTCCGCTG", "sample_one_TAGTGGAACT", "sample_one_TATCATGCAG",
+  "sample_one_TATCGTTACG", "sample_one_TCAAGTGCAG", "sample_one_TCACAGATAC",
+  "sample_one_TCACCGCCTA", "sample_one_TCACGCCACT", "sample_one_TCACGTTGGC",
+  "sample_one_TCATTGTCCA", "sample_one_TCCACACTAG", "sample_one_TCCACGGTCA",
+  "sample_one_TCCACTCGCT", "sample_one_TCCGACTAAC", "sample_one_TCCGTTATCT",
+  "sample_one_TCCTAAGAGA", "sample_one_TCCTCTAGTA", "sample_one_TCGAAGCATT",
+  "sample_one_TCGAGAGAGC", "sample_one_TCGCACTTGA", "sample_one_TCGCCTACTG",
+  "sample_one_TCGCGTAGCA", "sample_one_TCGGCGTTAA", "sample_one_TCTACATCCG",
+  "sample_one_TCTCCACATT", "sample_one_TCTCTCCTAT", "sample_one_TCTTGCTCGG",
+  "sample_one_TGAACTAACC", "sample_one_TGAAGAAGGT", "sample_one_TGAGCGTTCC",
+  "sample_one_TGAGTACGTA", "sample_one_TGGAATGGAG", "sample_one_TGTCATTCGC",
+  "sample_one_TGTGCTTCAG", "sample_one_TGTTCAGGAT", "sample_one_TTACACACGT",
+  "sample_one_TTACTGTGAC", "sample_one_TTATAGGAGG", "sample_one_TTATCGCGTT",
+  "sample_one_TTATGCCGCG", "sample_one_TTCACGGAAG", "sample_one_TTCAGGAGTA",
+  "sample_one_TTCCATCGAG", "sample_one_TTCGAGTGAT", "sample_one_TTCTGTACCT",
+  "sample_one_TTGGCAATTC", "sample_one_TTGGCTCCAC", "sample_one_TTGGTAACAG",
+  "sample_one_TTGGTCAGTA", "sample_one_TTGTCGGCCA", "sample_one_TTGTGTTCGA"
+)
+stopifnot(identical(sampleNames(sample_1_result), expected_sample_names))
+
+expected_var_labels <- c(
+  "WellBC",
+  "WellID",
+  "NumberOfMTReads",
+  "pctMT",
+  "NumberOfERCCReads",
+  "pctERCC",
+  "NumberOfChromReads",
+  "pctChrom",
+  "NumberOfInputReads",
+  "NumberOfMappedReads",
+  "PctMappedReads",
+  "NumberOfReadsMappedToMultipleLoci",
+  "PectOfReadsMappedToMultipleLoci",
+  "NumberOfReadsMappedToTooManyLoci",
+  "PectOfReadsMappedToTooManyLoci",
+  "NumberOfReadsUnmappedTooManyMismatches",
+  "PectOfReadsUnmappedTooManyMismatches",
+  "NumberOfReadsUnmappedTooShort",
+  "PectOfReadsUnmappedTooShort",
+  "NumberOfReadsUnmappedOther",
+  "PectOfReadsUnmappedOther",
+  "ReadsWithValidBarcodes",
+  "SequencingSaturation",
+  "Q30BasesInCB.UMI",
+  "ReadsMappedToTranscriptome.Unique.MultipeGenes",
+  "EstimatedNumberOfCells",
+  "FractionOfReadsInCells",
+  "MeanReadsPerCell",
+  "NumberOfUMIs",
+  "NumberOfGenes",
+  "NumberOfCountedReads",
+  "PoolName"
+)
+stopifnot(identical(varLabels(sample_1_result), expected_var_labels))
+
+read_mm <- function(mapping_dir) {
+  market_matrix_file <- file.path(mapping_dir, "Solo.out",
+                                  "Gene", "raw", "matrix.mtx")
+  result <- readMM(market_matrix_file)
+  feature_file <- file.path(mapping_dir, "Solo.out",
+                            "Gene", "raw", "features.tsv")
+  features <- read.table(feature_file, sep = "\t", header = FALSE,
+                         col.names = c("ID", "Name", "Type"))$ID
+  rownames(result) <- gsub("\\.\\d+$", "", features)
+  barcodes_file <- file.path(mapping_dir,
+                             "Solo.out", "Gene", "raw", "barcodes.tsv")
+  if (!file.exists(barcodes_file)) {
+    stop(paste0("Expected the 'Solo.out/Gene/raw' directory at ",
+                mapping_dir, " to contain a 'barcodes.tsv' file."))
+  }
+  barcodes <- readLines(barcodes_file)
+  if (length(barcodes) != 1) {
+    stop(paste0("A single STAR Solo folder should only have ",
+                "mapped one (1) barcode, but found '",
+                length(barcodes), "'for mapping directory ", mapping_dir))
+  }
+  colnames(result) <- paste0("sample_one_", barcodes)
+  return(result)
+}
+expected_matrices <- lapply(par$star_output, read_mm)
+expected_matrix <- as.matrix(do.call(cbind, expected_matrices))
+result_counts <- exprs(sample_1_result)
+stopifnot(length(setdiff(colnames(expected_matrix),
+                         colnames(exprs(sample_1_result)))) == 0)
+stopifnot(length(setdiff(rownames(expected_matrix),
+                         rownames(exprs(sample_1_result)))) == 0)
+expected_matrix_sorted <- expected_matrix[, colnames(exprs(sample_1_result))]
+stopifnot(identical(exprs(sample_1_result), expected_matrix_sorted))
--- a/src/integration_test_components/well_demultiplexing/check_cutatapt_output/config.vsh.yaml
+++ b/src/integration_test_components/well_demultiplexing/check_cutatapt_output/config.vsh.yaml
@@ -0,0 +1,41 @@
+name: "check_cutadapt_output"
+namespace: "integration_test_components/well_demultiplexing"
+description: "This component test the cutadapt output from the well_demultiplex subworkflow."
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ author, maintainer ]
+argument_groups:
+  - name: Inputs
+    arguments:
+      - name: "--fastq_r1"
+        type: file
+        required: true
+        multiple: true
+        description: Path to the forward reads to test.
+      - name: "--fastq_r2"
+        type: file
+        required: true
+        multiple: true
+        description: Path to the reverse reads to test.
+      - name: "--ids"
+        type: string
+        description: "Well IDs for the corresponding fastq input"
+        required: true
+        multiple: true
+resources:
+  - type: python_script
+    path: script.py
+engines:
+  - type: docker
+    image: python:3.12-slim
+    setup: 
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - dnaio
+
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/integration_test_components/well_demultiplexing/check_cutatapt_output/script.py
+++ b/src/integration_test_components/well_demultiplexing/check_cutatapt_output/script.py
@@ -0,0 +1,78 @@
+import dnaio
+from operator import itemgetter
+## VIASH START
+par = {
+}
+## VIASH END
+
+def assert_number_of_reads(reads):
+    expected_number_of_reads = {
+        "SRR14730301__A1": 165,
+        "SRR14730301__B1": 194,
+        "SRR14730302__A1": 141,
+        "SRR14730302__B1": 213,
+        "SRR14730302__unknown": 99646,
+        "SRR14730301__unknown": 99641,
+    }
+    for input_id, expected_reads in expected_number_of_reads.items():
+        num_reads = len(reads[input_id]) 
+        assert num_reads == expected_reads, \
+            f"Expected number of ouput reads for {input_id} to be {expected_reads}, was {num_reads}." 
+
+
+def string_difference(string1, string2):
+    result = 0
+    for char1, char2 in zip(string1, string2, strict=True):
+        if char1.lower() != char2.lower():
+            result += 1
+    return result
+
+
+def assert_barcodes_not_removed(reads):
+    barcodes = {
+        "SRR14730301__A1": "ACACCGAATT",
+        "SRR14730302__A1": "ACACCGAATT",
+        "SRR14730301__B1": "GGCTATTGAT",
+        "SRR14730302__B1": "GGCTATTGAT" 
+    }
+    for sample_id, barcode in barcodes.items():
+        sample_reads = reads[sample_id]
+        forward_reads = map(itemgetter(0), sample_reads)
+        for i, forward_read in enumerate(forward_reads):
+            read_sequence = forward_read.sequence
+            read_barcode_start = read_sequence[: len(barcode)]
+            # A 10% difference is allowed.
+            assert string_difference(read_barcode_start, barcode) <= (0.1 * len(barcode)), \
+                (f"Expected barcode {barcode} to be present for sample {sample_id} "
+                 f"in read {i}. Found {read_barcode_start}")
+
+def create_input_mapping(sample_ids, inputs_r1, inputs_r2):
+    return {sample_id: [input_r1, input_r2] 
+            for sample_id, input_r1, input_r2 
+            in zip(sample_ids, inputs_r1, inputs_r2, strict=True)}
+
+def read_input_files(input_mapping):
+    expected_keys = {"SRR14730301__A1", "SRR14730301__B1",
+                     "SRR14730302__A1", "SRR14730302__B1",
+                     "SRR14730301__unknown", "SRR14730302__unknown"}
+    difference = set(input_mapping.keys()) - expected_keys
+    assert not difference, f"Found unexpected output id(s): {difference}"
+    result = {}
+    for input_id, input_files in input_mapping.items():
+        input_r1, input_r2 = input_files
+        # This reads the files into memory,
+        # but they are reasonably small
+        with dnaio.open(input_r1) as r1_reads, dnaio.open(input_r2) as r2_reads:
+            for r1_read, r2_read in zip(r1_reads, r2_reads, strict=True):
+                result.setdefault(input_id, []).append((r1_read, r2_read))
+    return result
+
+
+def main(par):
+    inputs = create_input_mapping(par["ids"], par["fastq_r1"], par["fastq_r2"])
+    reads = read_input_files(inputs)
+    assert_number_of_reads(reads)
+    assert_barcodes_not_removed(reads)
+
+if __name__ == "__main__":
+    main(par)
--- a/src/io/publish_fastqs/code.sh
+++ b/src/io/publish_fastqs/code.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+echo "Publishing $par_input -> $par_output"
+
+echo
+echo "Creating directory if it does not exist:"
+mkdir -p "$par_output" && echo "$par_output created"
+
+echo
+echo "Copying files..."
+IFS=";" read -ra input <<<$par_input
+
+for i in "${input[@]}"; do
+  cp -rL "$i" "$par_output/"
+done
--- a/src/io/publish_fastqs/config.vsh.yaml
+++ b/src/io/publish_fastqs/config.vsh.yaml
@@ -0,0 +1,34 @@
+name: "publish_fastqs"
+namespace: "io"
+description: "Publish the fastq files per well"
+argument_groups:
+  - name: Input arguments
+    arguments:
+      - name: --input
+        description: Directory to write fastq data to
+        type: file
+        multiple: true
+        required: true
+  - name: Output arguments
+    arguments:
+      - name: --output
+        type: file
+        direction: output
+        # ID is the well barcode
+        default: "$id/"
+
+resources:
+  - type: bash_script
+    path: ./code.sh
+
+engines:
+  - type: docker
+    image: debian:stable-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/io/publish_results/code.sh
+++ b/src/io/publish_results/code.sh
@@ -0,0 +1,93 @@
+#!/bin/bash
+
+set -eo pipefail
+
+echo "Publishing results to multiple output directories"
+
+# Create output directories for multiple files
+echo "Creating output directories..."
+
+path_pars=(
+  par_star_output_dir
+  par_nrReadsNrGenesPerChrom_dir
+  par_star_qc_metrics_dir
+  par_eset_dir
+  par_f_data_dir
+  par_p_data_dir
+  par_html_report_output
+  par_run_params_output
+)
+
+for par in ${path_pars[@]}; do
+    curr_val="${!par}"
+    new_value=$(realpath --canonicalize-missing "$curr_val")
+    declare -g "$par=$new_value"
+done
+
+mkdir -p "$par_star_output_dir" && echo "$par_star_output_dir created"
+mkdir -p "$par_nrReadsNrGenesPerChrom_dir" && echo "$par_nrReadsNrGenesPerChrom_dir created"  
+mkdir -p "$par_star_qc_metrics_dir" && echo "$par_star_qc_metrics_dir created"
+mkdir -p "$par_eset_dir" && echo "$par_eset_dir created"
+mkdir -p "$par_f_data_dir" && echo "$par_f_data_dir created"
+mkdir -p "$par_p_data_dir" && echo "$par_p_data_dir created"
+
+echo
+echo "Copying STAR output files..."
+IFS=";" read -ra star_output <<<$par_star_output
+for i in "${star_output[@]}"; do
+  echo "Copying $i to $par_star_output_dir/"
+  cp -rL "$i" "$par_star_output_dir/"
+done
+
+echo
+echo "Copying nrReadsNrGenesPerChrom files..."
+IFS=";" read -ra nrReadsNrGenesPerChrom <<<$par_nrReadsNrGenesPerChrom
+for i in "${nrReadsNrGenesPerChrom[@]}"; do
+  echo "Copying $i to $par_nrReadsNrGenesPerChrom_dir/"
+  cp -rL "$i" "$par_nrReadsNrGenesPerChrom_dir/"
+done
+
+echo
+echo "Copying STAR QC metrics files..."
+IFS=";" read -ra star_qc_metrics <<<$par_star_qc_metrics
+for i in "${star_qc_metrics[@]}"; do
+  echo "Copying $i to $par_star_qc_metrics_dir/"
+  cp -rL "$i" "$par_star_qc_metrics_dir/"
+done
+
+echo
+echo "Copying eset files..."
+IFS=";" read -ra eset <<<$par_eset
+for i in "${eset[@]}"; do
+  echo "Copying $i to $par_eset_dir/"
+  cp -rL "$i" "$par_eset_dir/"
+done
+
+echo
+echo "Copying f_data files..."
+IFS=";" read -ra f_data <<<$par_f_data
+for i in "${f_data[@]}"; do
+  echo "Copying $i to $par_f_data_dir/"
+  cp -rL "$i" "$par_f_data_dir/"
+done
+
+echo
+echo "Copying p_data files..."
+IFS=";" read -ra p_data <<<$par_p_data
+for i in "${p_data[@]}"; do
+  echo "Copying $i to $par_p_data_dir/"
+  cp -rL "$i" "$par_p_data_dir/"
+done
+
+echo
+echo "Copying single files directly..."
+mkdir -p $(dirname "$par_html_report_output")
+echo "Copying $par_html_report to $par_html_report_output"
+cp -L "$par_html_report" "$par_html_report_output"
+
+echo "Copying $par_run_params to $par_run_params_output"
+mkdir -p $(dirname "$par_run_params_output")
+cp -L "$par_run_params" "$par_run_params_output"
+
+echo
+echo "Publishing completed successfully!"
--- a/src/io/publish_results/config.vsh.yaml
+++ b/src/io/publish_results/config.vsh.yaml
@@ -0,0 +1,91 @@
+name: "publish_results"
+namespace: "io"
+description: "Publish the results"
+argument_groups:
+  - name: Input arguments
+    arguments:
+      - name: --star_output
+        description: Output from mapping with STAR
+        type: file
+        multiple: true
+        required: true
+      - name: "--nrReadsNrGenesPerChrom"
+        type: file
+        multiple: true
+        required: true
+      - name: "--star_qc_metrics"
+        type: file
+        multiple: true
+        required: true
+      - name: "--eset"
+        type: file
+        multiple: true
+        required: true
+      - name: "--f_data"
+        type: file
+        multiple: true
+        required: true
+      - name: "--p_data"
+        type: file
+        multiple: true
+        required: true
+      - name: "--html_report"
+        type: file
+        required: true
+      - name: "--run_params"
+        type: file
+        required: true
+  - name: Output directory
+    description: |
+      Determines the name of output directories
+    arguments:
+      - name: --star_output_dir
+        type: file
+        direction: output
+        default: "star_output"
+      - name: --nrReadsNrGenesPerChrom_dir
+        type: file
+        direction: output
+        default: "nrReadsNrGenesPerChrom"
+      - name: --star_qc_metrics_dir
+        type: file
+        direction: output
+        default: "starLogs"
+      - name: --eset_dir
+        type: file
+        direction: output
+        default: "esets"
+      - name: --f_data_dir
+        type: file
+        direction: output
+        default: "fData"
+      - name: --p_data_dir
+        type: file
+        direction: output
+        default: "pData"
+  - name: "Output file arguments"
+    description: Determines the name of output files
+    arguments:
+      - name: "--run_params_output"
+        type: file
+        direction: output
+      - name: "--html_report_output"
+        type: file
+        direction: output
+
+
+resources:
+  - type: bash_script
+    path: ./code.sh
+
+engines:
+  - type: docker
+    image: debian:stable-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/parallel_map/STAR
+++ b/src/parallel_map/STAR
--- a/src/parallel_map/config.vsh.yaml
+++ b/src/parallel_map/config.vsh.yaml
@@ -0,0 +1,124 @@
+name: parallel_map
+description: |
+  Map wells in batch, using STAR
+  Spliced Transcripts Alignment to a Reference (C) Alexander Dobin
+  https://github.com/alexdobin/STAR
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ maintainer ]
+  - __merge__: /src/base/authors/toni_verbeiren.yaml
+    roles: [ author, maintainer ]
+requirements:
+  commands:
+    - STAR
+    - file
+    - parallel
+argument_groups:
+  - name: Input arguments
+    arguments:
+    - name: "--input_r1"
+      description: |
+        Input FASTQ files for the forward reads. All FASTQ file names must start with the prefix '{well_id}_R1', where
+        'well_id' can be found as the sequence identifier in the barcodes FASTA file (see 'barcodesFasta' argument).
+        For each FASTQ file, a matching FASTQ file for the reverse reads must be provided to the 'input_r2' argument,
+        meaning that their 'well_id' prefix must match. The number of items provided for 'input_r1' must be equal
+        to the number of items for 'input_r2'.
+      type: file
+      required: true
+      multiple: true
+    - name: "--input_r2"
+      description: |
+        Input FASTQ files for the reverse reads. All FASTQ file names must start with the prefix '{well_id}_R2', where
+        'well_id' can be found as the sequence identifier in the barcodes FASTA file (see 'barcodesFasta' argument).
+        For each FASTQ file, a matching FASTQ file for the reverse reads must be provided to the 'input_r1' argument,
+        meaning that their 'well_id' prefix must match. The number of items provided for 'input_r1' must be equal
+        to the number of items for 'input_r2'.
+      type: file
+      required: true
+      multiple: true
+    - name: "--genomeDir"
+      description: |
+        Reference genome to match to. Can be generated from genomic FASTA sequences and a genome annotation
+        by using STAR with '--runMode genomeGenerate'.
+      type: file
+      required: true
+    - name: "--barcodesFasta"
+      type: file
+      required: true
+      description: |
+        FASTA file where each entry specifies a unique barcode sequence present at the start of the forward input reads
+        (input_r1). The IDs of each barcode (the start of the FASTA headers up until the first whitespace character) must
+        match with the start of one input FASTQ pair.
+  - name: Barcode arguments
+    arguments:
+    - name: "--umiLength"
+      type: integer
+      required: true
+      description: |
+        Length of the Unique Molecular Identifiers (UMI). The UMI are expected to be located after the barcodes in the
+        forwards reads.
+    - name: "--limitBAMsortRAM"
+      type: string
+      default: "10000000000"
+  - name: Runtime arguments
+    arguments:
+    - name: "--runThreadN"
+      description: "Number of threads to use for a single STAR execution."
+      type: integer
+      default: 1
+  - name: Output arguments
+    arguments:
+    - name: "--output"
+      type: file
+      description: |
+          A list of output folders which are the result of using STAR to map each input FASTQ pair STAR to the reference genome.
+          The order of the items DO NOT match with the order of the entries in the barcodes FASTA file or the input FASTQ pairs. 
+      required: true
+      multiple: true
+      direction: output
+      default: './*'
+    - name: "--joblog"
+      type: file
+      description: Where to store the log file listing all the jobs.
+      required: false
+      direction: output
+      default: "execution_log.txt"
+
+resources:
+- type: bash_script
+  path: script.sh
+- path: STAR
+
+test_resources:
+  - type: bash_script
+    path: test.sh
+
+engines:
+  - type: docker
+    image: debian:stable-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+          - wget
+          - automake
+          - make
+          - gcc
+          - g++
+          - zlib1g-dev
+          - parallel
+          - file
+          - seqkit
+      - type: docker
+        build_args:
+          - STAR_V=2.7.6a
+        env:
+          - STAR_SOURCE="https://github.com/alexdobin/STAR/archive/refs/tags/$STAR_V.tar.gz"
+          - STAR_TARGET="/app/star-$STAR_V.tar.gz"
+          - STAR_INSTALL_DIR="/app/STAR-$STAR_V"
+          - STAR_BINARY=STAR
+        copy:
+          - STAR /usr/local/bin/$STAR_BINARY
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/parallel_map/script.sh
+++ b/src/parallel_map/script.sh
@@ -0,0 +1,342 @@
+#!/bin/bash
+
+## VIASH START
+par_input_r1="work/2c/5b8b3a2dd4a988b8838e3f72d38a37/_viash_par/input_r1_1/two__ACACCGAATT.concat_text_r1.output.txt"
+par_input_r2="work/2c/5b8b3a2dd4a988b8838e3f72d38a37/_viash_par/input_r2_1/two__ACACCGAATT.concat_text_r2.output.txt"
+par_barcodes="ACACCGAATT;GGCTATTGAT"
+par_output="./*"
+par_genomeDir="star"
+par_umiLength=10
+par_limitBAMsortRAM="10000000000"
+meta_cpus=2
+par_runThreadN=1
+## VIASH END
+
+set -eo pipefail
+
+# Check if wildcard character is present in output folder template
+printf "Checking if output folder template ($par_output) contains a single wildcard character '*'. "
+output_glob_character="${par_output//[^\*]}"
+if [[ "${#output_glob_character}" -ne "1" ]]; then
+  echo "The value for --output must contain exactly one '*' character. Exiting..."
+  exit 1
+else
+  echo "Done, wildcard character found!"
+fi
+
+# Split the delimited strings into arrays
+IFS=';' read -r -a input_r1 <<< "$par_input_r1"
+IFS=';' read -r -a input_r2 <<< "$par_input_r2"
+
+# Read barcodes FASTQ
+# seqkit will make sure to take the leading non-whitespace as sequence identifier (ID)
+# Luckily, this is the same as how cutadapt determines an adapter name from the FASTA header.
+readarray -t well_ids < <(seqkit seq --name "$par_barcodesFasta" )
+readarray -t barcodes < <(seqkit seq --seq --upper-case --remove-gaps --gap-letters '^' --validate-seq "$par_barcodesFasta")
+
+# Function to test for unique values in array
+function arrayContainsUniqueValues {
+  # Pass the argument by reference
+  local -n arr=$1
+  # Create a temporary associative array
+  # in order to use its uniqueness of keys
+  # 'declare' in a function is automatically local
+  declare -A uniq_tmp
+  for item in "${arr[@]}"; do
+    uniq_tmp[$item]=0 # assigning a placeholder
+  done
+  local unique_array_values=(${!uniq_tmp[@]})
+  if [ "${#unique_array_values[@]}" -eq "${#arr[@]}" ]; then
+    return
+  fi
+  false
+}
+arrayContainsUniqueValues barcodes
+is_array_unique_exit_code=$?
+if ! (exit $is_array_unique_exit_code); then 
+  echo "The provided barcodes should be unique!"
+  echo "Values: $par_barcodes"
+  exit 1
+fi
+
+# Check that the number of values provided for the fastq files are the same.
+num_r1_inputs="${#input_r1[@]}"
+num_r2_inputs="${#input_r2[@]}"
+
+if [ ! "$num_r1_inputs" -eq "$num_r2_inputs" ]; then
+  echo "The number of values for arguments "\
+        "'input_r1' ($num_r1_inputs) and 'input_r2' ($num_r2_inputs) "\
+        "should be the same."
+  exit 1
+else
+  echo "Checked if the same as the number of R1 FASTQ ($num_r1_inputs) and R2 FASTQ files "\
+       "($num_r2_inputs) were provided. Seems OK!"
+fi
+
+# Loop over the well IDs and match them to the input FASTQ files
+# The FASTQ file names should have the format {well_id}_R(1|2).fastq,
+# which is the output format that the cutadapt component uses for demultiplexing.
+# sorted_input_r1 and sorted_input_r2 are the input FASTQ files sorted by the order
+# of the barcodes in the barcodes array (i.e. the order in the barcodes FASTA file).
+declare -a sorted_input_r1=()
+declare -a sorted_input_r2=()
+for barcode_index in "${!barcodes[@]}"; do
+  barcode="${barcodes[$barcode_index]}"
+  well_id="${well_ids[$barcode_index]}"
+  echo "Finding FASTQ files for barcode ${barcode}, well ID '${well_id}'."
+  # The FASTQ files for a particular barcode must match the following regex:
+  input_file_regex="^${well_id}_R[1-2]"
+  
+  for r1_index in "${!input_r1[@]}"; do
+    r1_file_path=${input_r1[$r1_index]}
+    r2_file_path=${input_r2[$r1_index]}
+    # Get the file names from the full path
+    r1_file_name=$(basename -- "$r1_file_path")
+    r2_file_name=$(basename -- "$r2_file_path")
+
+    # Check if the file names match the regex
+    if [[ $r1_file_name =~ $input_file_regex ]]; then
+      echo "Matched with $r1_file_name and $r2_file_name."
+      # If the R1 FASTQ file matched the regex, 
+      # the R2 file must have also been matched
+      if ! [[ $r2_file_name =~ $input_file_regex ]]; then
+        echo "File ${r1_file_name} matched with regex ${input_file_regex} "\
+          "but ${r2_file_name} did not! Make sure that the order of "\
+          "the R1 and R2 input files match."
+        exit 1
+      fi
+      # Add the 
+      sorted_input_r1+=("$r1_file_path")
+      sorted_input_r2+=("$r2_file_path")
+      # Do not continue looking for more files for this barcode
+      # '2' to affect the *outer* loop (which indeed loops barcodes)!
+      continue 2
+    fi
+  done
+  echo "Did not find FASTQ files files for well ${well_id}! "\
+    "Make sure that the input files have the correct file name format."\
+    "Input files: ${input_r1[@]}"
+  exit 1
+done
+
+
+# Define the function that will be used to run a single job
+function _run() {
+  local par_UMIlength="$1"
+  local par_output="$2"
+  local par_genomeDir="$3"
+  local par_limitBAMsortRAM="$4"
+  local par_runThreadN="$5"
+  local barcode="$6"
+  local input_R1="$7"
+  local input_R2="$8"
+
+  local barcode_length="${#barcode}"
+  local umi_start="$(($barcode_length + 1))"
+
+  set -eo pipefail
+
+  echo <<-EOF
+    Processing $barcode
+    For the following inputs (lanes):
+    "$star_readFilesIn
+	EOF
+
+  echo "Writing barcode '$barcode' to $barcode.txt and using it as input".
+  # Note that there is no possible conflict between jobs here
+  # because the barcodes are unique (and the barcode is part of the name
+  # of the file).
+  echo "$barcode" > "$barcode.txt"
+
+  local dir="${par_output//\*/$barcode}/"
+  echo "Setting output for barcode '$barcode' to '$dir'."
+  mkdir -p "$dir"
+
+  # check if files are compressed
+  local TMPDIR=$(mktemp -d "$meta_temp_dir/parallel_map-$barcode-XXXXXX")
+  function clean_up {
+    [[ -d "$TMPDIR" ]] && rm -r "$TMPDIR"
+  }
+  trap clean_up RETURN
+
+  # Decompress the input files when needed
+  # NOTE: for some reason, using STAR's --readFilesCommand does not always work
+  # This might be because STAR creates fifo files (see https://man7.org/linux/man-pages/man7/fifo.7.html)
+  # and this requires a filesystem that supports this. Another cause might be that the input files
+  # are symlinks. When testing this, using '--readFilesCommand "zcat"' 
+  # always produced empty BAM files, but also a succesfull exit code (0) so the problem is not reported.
+  # However, the logs showed the following error: "gzip -: unexpected end of file".
+
+  function is_gzipped {
+    printf "Checking if input '$1' (barcode '$barcode') is gzipped... "
+    if file "$1" | grep -q 'gzip'; then
+      echo "Done, detected compressed file."
+      return
+    fi
+    echo "Done, file does not need decompression."
+    false
+  }
+  
+  # Resolve symbolic links to actual file paths
+  input_R1=$(realpath $input_R1)
+  input_R2=$(realpath $input_R2)
+
+  if is_gzipped $input_R1; then
+    local compressed_file_name_r1="$(basename -- $input_R1)"
+    local uncompressed_file_r1="$TMPDIR/${compressed_file_name_r1%.gz}"
+    printf "Unpacking input to $uncompressed_file_r1... "
+    zcat "$input_R1" > "$uncompressed_file_r1"
+    echo "Decompression done."
+  else
+    local uncompressed_file_r1="$input_R1"
+  fi
+
+  if is_gzipped $input_R2; then
+    local compressed_file_name_r2="$(basename -- $input_R2)"
+    local uncompressed_file_r2="$TMPDIR/${compressed_file_name_r2%.gz}"
+    printf "Unpacking input to $uncompressed_file_r2... "
+    zcat "$input_R2" > "$uncompressed_file_r2"
+    echo "Decompression done."
+  else
+    local uncompressed_file_r2="$input_R2"
+  fi
+
+  local n_input_lines_r1=$(wc -l < "$uncompressed_file_r1")
+  local n_input_lines_r2=$(wc -l < "$uncompressed_file_r2")
+
+  printf "Checking if length of input file mates match. "
+  if (( $n_input_lines_r1 != n_input_lines_r2 )); then
+    echo "The length of file $input_R1 ($n_input_lines_r1) does not match with $input_R2 ($n_input_lines_r2)"
+    return 1
+  else
+    echo "Seems OK, $n_input_lines_r1 input lines."
+  fi
+  echo "Starting STAR for barcode '$barcode'"
+  # soloType 'Droplet' is the same as 'CB_UMI_Simple': one UMI and one cell barcode of fixed length. 
+  # By default in this mode, STAR will look for the cell barcode and the UMI int the last files specified with --readFilesIn
+  # So we need to specify R2 first and R1 second, because R1 contains the barcode and UMI.
+  # Also, you might be tempted to use '--soloBarcodeMate 1' to alter this behavior, but this requires the clipping
+  # the barcode from this mate by specifying --clip5pNbases and/or --clip3pNbases, which we do not want to do.
+  STAR \
+    --readFilesIn "$uncompressed_file_r2" "$uncompressed_file_r1" \
+    --soloType Droplet \
+    --quantMode GeneCounts \
+    --genomeLoad LoadAndKeep \
+    --limitBAMsortRAM "$par_limitBAMsortRAM" \
+    --runThreadN "$par_runThreadN" \
+    --outFilterMultimapNmax 1 \
+    --outSAMtype BAM SortedByCoordinate \
+    --soloCBstart 1 \
+    --readFilesType "Fastx" \
+    --soloCBlen "$barcode_length" \
+    --soloUMIstart "$umi_start" \
+    --soloUMIlen "$par_UMIlength" \
+    --soloBarcodeReadLength 0 \
+    --soloStrand Unstranded \
+    --soloFeatures Gene \
+    --genomeDir "$par_genomeDir" \
+    --outReadsUnmapped Fastx \
+    --outSAMunmapped Within \
+    --outSAMattributes NH HI nM AS CR UR CB UB GX GN \
+    --soloCBwhitelist "$barcode.txt" \
+    --outFileNamePrefix "$dir" \
+    --outTmpDir "$TMPDIR/STARtemp/"
+
+  printf "Done running STAR. "
+  # Check if the number of processed reads is equal to the number of input reads
+  local n_input_reads=$(($n_input_lines_r1 / 4))
+  local nr_output_reads=$(grep -Po "Number\ of\ input\ reads \\|\W*\K\d+" "$dir/Log.final.out")
+  if (( $nr_output_reads != $n_input_reads )); then
+    echo "Not all input reads were processed for barcode $barcode."
+    return 1
+  else
+    echo "Processed $nr_output_reads reads for barcode $barcode".
+  fi
+
+  printf "Making sure that the output has the proper permissions."
+  find "$dir" -type d -exec chmod o+x {} \;
+  chmod -R o+r "$dir"
+  echo "Done"
+}
+
+# Export the function - requires bash
+export -f _run
+
+# Load reference genome
+echo "Loading reference genome"
+STAR --genomeLoad LoadAndExit --genomeDir "$par_genomeDir"
+
+# Run the concurrent jobs using GNU parallel
+
+# Make sure that parallel uses the correct shell
+export PARALLEL_SHELL="/bin/bash"
+
+# Some notes:
+#   --halt now,fail=1: instruct parallel to exit when a job has failed and kill remaining running jobs.
+#   
+# ::: is a special syntax for GNU parallel to delineate inputs
+# If multiple ::: are given, each group will be treated as an input source, and all combinations of input
+# sources will be generated. E.g. ::: 1 2 ::: a b c will result in the combinations (1,a) (1,b) (1,c) (2,a) (2,b) (2,c)
+# The delimiter :::+ (note the extra '+') links the argument to the previous argument, and one argument from each of the input
+# sources will be read.
+parallel_cmd=("parallel" "--jobs" "80%" "--verbose" "--memfree" "2G"
+              "--tmpdir" "$meta_temp_dir"
+              "--retry-failed" "--retries" "4" "--halt" "soon,fail=1"
+              "--joblog" "$par_joblog" "_run" "{}")
+
+# Arguments for which there is one value, so these will not create extra jobs
+parallel_cmd+=(":::" "$par_umiLength" ":::" "$par_output" ":::" "$par_genomeDir" ":::" "$par_limitBAMsortRAM" ":::" "$par_runThreadN")
+
+# Argument which in fact will cause extra jobs to be spawned, per job one item from each argument will be selected
+# Thus, these argument lists should have the same length.
+parallel_cmd+=(":::" "${barcodes[@]}" ":::+" "${sorted_input_r1[@]}" ":::+" "${sorted_input_r2[@]}")
+
+set +eo pipefail
+"${parallel_cmd[@]}"
+exit_code=$?
+set -eo pipefail
+
+echo "GNU parallel finished!"
+
+# Unload reference
+printf "Unloading reference genome. "
+STAR --genomeLoad Remove --genomeDir "$par_genomeDir"
+echo "Done!"
+
+# Exit code from GNU parallel:
+# If fail=1 is used, the exit status will be the exit status of the failing job.
+echo "Checking exit code"
+if ((exit_code>0)); then
+  # Note that the ending HERE must be indented with TAB characters (not spaces)
+  # in order to remove leading indentation
+  MESSAGE=$(
+    cat <<-HERE
+    ==================================================================
+
+    !!! An error occurred for one of the jobs.
+    Exit code of the failing job: $exit_code.
+
+    %s
+
+    ==================================================================
+
+		HERE
+  )
+  printf "$MESSAGE" "$(<$par_joblog)"
+  exit 1
+else
+  cat <<-HERE
+  ==================================================================
+
+  Mapping went fine (exit code '$exit_code'), zero errors occurred
+
+  ==================================================================
+	HERE
+
+fi
+
+
+
+
+
+
--- a/src/parallel_map/test.sh
+++ b/src/parallel_map/test.sh
@@ -0,0 +1,466 @@
+set -eo pipefail
+
+## VIASH START
+meta_executable=$(realpath "target/executable/parallel_map/parallel_map")
+## VIASH END
+
+# Some helper functions
+assert_directory_exists() {
+  [ -d "$1" ] || { echo "File '$1' does not exist" && exit 1; }
+}
+
+assert_file_exists() {
+  [ -f "$1" ] || { echo "File '$1' does not exist" && exit 1; }
+}
+
+assert_file_contains() {
+  grep -q "$2" "$1" || { echo "File '$1' does not contain '$2'" && exit 1; }
+}
+
+assert_file_contains_regex() {
+  grep -q -E "$2" "$1" || { echo "File '$1' does not contain '$2'" && exit 1; }
+}
+
+echo "> Prepare test data in $meta_temp_dir"
+TMPDIR=$(mktemp -d --tmpdir="$meta_temp_dir")
+function clean_up {
+  [[ -d "$TMPDIR" ]] && rm -r "$TMPDIR"
+}
+trap clean_up EXIT
+
+# Sample 1, barcode ACAGTCACAG, UMI CTACGGATGA
+cat > "$TMPDIR/sample1_R1.fastq" <<'EOF'
+@SAMPLE_1_SEQ_ID1
+ACAGTCACAGCTACGGATGAGCCTCATAAGCCTCACACATCCGCGCCTATGTTGTGACTCTCTGTGAG
+
+IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
+@SAMPLE_1_SEQ_ID2
+ACAGTCACAGCTACGGATGAGCCTCATAAGCCTCACACATCCGCGCCTATGTTGTGACTCTCTGTGAG
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+EOF
+
+cat > "$TMPDIR/sample1_R2.fastq" <<'EOF'
+@SAMPLE_1_SEQ_ID1
+CTCACAGAGAGTCACAACATAGGCGCGGATGTGTGAGGCTTATGAGGC
+
+IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
+@SAMPLE_1_SEQ_ID2
+CTCACAGAGAGTCACAACATAGGCGCGGATGTGTGAGGCTTATGAGGC
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+EOF
+
+# Sample 2, barcode CGGGTTTACC, UMI GCTAGCTAGC
+cat > "$TMPDIR/sample2_R1.fastq" << 'EOF'
+@SAMPLE_2_SEQ_ID1
+CGGGTTTACCGCTAGCTAGCCACCACTATGGTTGGCCGGTTAGTAGTGT
+
+IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
+@SAMPLE_2_SEQ_ID2
+CGGGTTTACCGCTAGCTAGCCACCACTATGGTTGGCCGGTTAGTAGTGT
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+EOF
+
+cat > "$TMPDIR/sample2_R2.fastq" <<'EOF'
+@SAMPLE_2_SEQ_ID1
+ACACTACTAACCGGCCAACCATAGTGGTG
+
+IIIIIIIIIIIIIIIIIIIIIIIIIIIII
+@SAMPLE_2_SEQ_ID2
+ACACTACTAACCGGCCAACCATAGTGGTG
+
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+EOF
+
+cat > "$TMPDIR/barcodes.fasta" <<'EOF'
+>sample1
+ACAGTCACAG
+>sample2
+CGGGTTTACC
+EOF
+
+# Note that there is a sjdbGTFchrPrefix argument for STAR:
+# prefix for chromosome names in a GTF file (default: '-')
+cat > "$TMPDIR/genome.fasta" <<'EOF'
+>1
+TGGCATGAGCCAACGAACGCTGCCTCATAAGCCTCACACATCCGCGCCTATGTTGTGACTCTCTGTGAGCGTTCGTGGG
+GCTCGTCACCACTATGGTTGGCCGGTTAGTAGTGTGACTCCTGGTTTTCTGGAGCTTCTTTAAACCGTAGTCCAGTCAA
+TGCGAATGGCACTTCACGACGGACTGTCCTTAGCTCAGGGGA
+EOF
+
+cat > "$TMPDIR/genes.gtf" <<'EOF'
+1    example_source  gene       0    72   .   +   .   gene_id "gene1"; gene_name: "GENE1;
+1    example_source  exon       20   71   .   +   .   gene_id "gene1"; gene_name: "GENE1"; exon_id: gene1_exon1;
+1    example_source  gene       80   160   .   +   .   gene_id "gene2"; gene_name: "GENE2;
+1    example_source  exon       80   159   .   +   .   gene_id "gene2"; gene_name: "GENE2"; exon_id: gene2_exon1;
+
+EOF
+
+echo "> Generate index"
+STAR \
+  ${meta_cpus:+--runThreadN $meta_cpus} \
+  --runMode genomeGenerate \
+  --genomeDir "$TMPDIR/index/" \
+  --genomeFastaFiles "$TMPDIR/genome.fasta" \
+  --sjdbGTFfile "$TMPDIR/genes.gtf" \
+  --genomeSAindexNbases 2 > /dev/null 2>&1
+
+
+echo "> Run test 1"
+run_1_dir="$TMPDIR/run_1"
+mkdir -p "$run_1_dir"
+pushd "$run_1_dir" > /dev/null
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample1_R1.fastq;$TMPDIR/sample2_R1.fastq" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq;$TMPDIR/sample2_R2.fastq" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_*"
+popd
+
+echo ">> Check if output directories exists"
+sample1_out="$TMPDIR/output_ACAGTCACAG"
+sample2_out="$TMPDIR/output_CGGGTTTACC"
+assert_directory_exists "$sample1_out"
+assert_directory_exists "$sample2_out"
+
+echo ">> Check if output files have been created"
+for sample in "$sample1_out" "$sample2_out"; do
+  assert_file_exists "$sample/Aligned.sortedByCoord.out.bam" 
+  assert_file_exists "$sample/Unmapped.out.mate1"
+  assert_file_exists "$sample/Unmapped.out.mate2"
+  assert_file_exists "$sample/Log.out"
+  assert_file_exists "$sample/Log.final.out"
+  assert_file_exists "$sample/ReadsPerGene.out.tab"
+done 
+
+
+echo ">> Check if Solo output is present"
+for sample in "$sample1_out" "$sample2_out"; do
+  assert_directory_exists "$sample1_out/Solo.out"
+  assert_directory_exists "$sample1_out/Solo.out/Gene"
+  assert_file_exists "$sample1_out/Solo.out/Barcodes.stats"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/barcodes.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/features.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/matrix.mtx"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/barcodes.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/features.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/matrix.mtx"
+done
+
+echo ">> Check contents of output"
+echo ">>> Sample 1"
+assert_file_contains "$sample1_out/Solo.out/Barcodes.stats" "nExactMatch              2"
+assert_file_contains "$sample1_out/Log.final.out" "Uniquely mapped reads number |	2"
+assert_file_contains "$sample1_out/Log.final.out" "Number of input reads |	2"
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/barcodes.tsv" || { echo "Barcodes file is different"; exit 1; }
+ACAGTCACAG
+EOF
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/features.tsv" || { echo "Features file is different"; exit 1; }
+gene1	gene1	Gene Expression
+gene2	gene2	Gene Expression
+EOF
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/matrix.mtx" || { echo "Matrix file is different"; exit 1; }
+%%MatrixMarket matrix coordinate integer general
+%
+2 1 1
+1 1 1
+EOF
+
+echo ">>> Sample 2"
+assert_file_contains "$sample2_out/Solo.out/Barcodes.stats" "nExactMatch              2"
+assert_file_contains "$sample2_out/Log.final.out" "Uniquely mapped reads number |	2"
+assert_file_contains "$sample2_out/Log.final.out" "Number of input reads |	2"
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/barcodes.tsv" || { echo "Barcodes file is different"; exit 1; }
+CGGGTTTACC
+EOF
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/features.tsv" || { echo "Features file is different"; exit 1; }
+gene1	gene1	Gene Expression
+gene2	gene2	Gene Expression
+EOF
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/matrix.mtx" || { echo "Matrix file is different"; exit 1; }
+%%MatrixMarket matrix coordinate integer general
+%
+2 1 1
+2 1 1
+EOF
+
+echo "> Run test 2 (compressed input)"
+gzip -c "$TMPDIR/sample1_R1.fastq" > "$TMPDIR/sample1_R1.fastq.gz"
+gzip -c "$TMPDIR/sample2_R1.fastq" > "$TMPDIR/sample2_R1.fastq.gz"
+gzip -c "$TMPDIR/sample1_R2.fastq" > "$TMPDIR/sample1_R2.fastq.gz"
+gzip -c "$TMPDIR/sample2_R2.fastq" > "$TMPDIR/sample2_R2.fastq.gz"
+
+run_2_dir="$TMPDIR/run_2"
+mkdir -p "$run_2_dir" 
+pushd "$run_2_dir" > /dev/null
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample1_R1.fastq.gz;$TMPDIR/sample2_R1.fastq.gz" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq.gz;$TMPDIR/sample2_R2.fastq.gz" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_gz_*" > /dev/null 2>&1
+popd > /dev/null
+
+echo ">> Check if output directories exists"
+sample1_out="$TMPDIR/output_gz_ACAGTCACAG"
+sample2_out="$TMPDIR/output_gz_CGGGTTTACC"
+assert_directory_exists "$sample1_out"
+assert_directory_exists "$sample2_out"
+
+echo ">> Check if output files have been created"
+for sample in "$sample1_out" "$sample2_out"; do
+  assert_file_exists "$sample/Aligned.sortedByCoord.out.bam" 
+  assert_file_exists "$sample/Unmapped.out.mate1"
+  assert_file_exists "$sample/Unmapped.out.mate2"
+  assert_file_exists "$sample/Log.out"
+  assert_file_exists "$sample/Log.final.out"
+  assert_file_exists "$sample/ReadsPerGene.out.tab"
+done 
+
+
+echo ">> Check if Solo output is present"
+for sample in "$sample1_out" "$sample2_out"; do
+  assert_directory_exists "$sample1_out/Solo.out"
+  assert_directory_exists "$sample1_out/Solo.out/Gene"
+  assert_file_exists "$sample1_out/Solo.out/Barcodes.stats"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/barcodes.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/features.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/matrix.mtx"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/barcodes.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/features.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/matrix.mtx"
+done
+
+echo ">> Check contents of output"
+echo ">>> Sample 1"
+assert_file_contains "$sample1_out/Solo.out/Barcodes.stats" "nExactMatch              2"
+assert_file_contains "$sample1_out/Log.final.out" "Uniquely mapped reads number |	2"
+assert_file_contains "$sample1_out/Log.final.out" "Number of input reads |	2"
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/barcodes.tsv" || { echo "Barcodes file is different"; exit 1; }
+ACAGTCACAG
+EOF
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/features.tsv" || { echo "Features file is different"; exit 1; }
+gene1	gene1	Gene Expression
+gene2	gene2	Gene Expression
+EOF
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/matrix.mtx" || { echo "Matrix file is different"; exit 1; }
+%%MatrixMarket matrix coordinate integer general
+%
+2 1 1
+1 1 1
+EOF
+
+echo ">>> Sample 2"
+assert_file_contains "$sample2_out/Solo.out/Barcodes.stats" "nExactMatch              2"
+assert_file_contains "$sample2_out/Log.final.out" "Uniquely mapped reads number |	2"
+assert_file_contains "$sample2_out/Log.final.out" "Number of input reads |	2"
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/barcodes.tsv" || { echo "Barcodes file is different"; exit 1; }
+CGGGTTTACC
+EOF
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/features.tsv" || { echo "Features file is different"; exit 1; }
+gene1	gene1	Gene Expression
+gene2	gene2	Gene Expression
+EOF
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/matrix.mtx" || { echo "Matrix file is different"; exit 1; }
+%%MatrixMarket matrix coordinate integer general
+%
+2 1 1
+2 1 1
+EOF
+
+
+cat > "$TMPDIR/wrong_number_of_barcodes.fasta" <<'EOF'
+>A1
+ACAGTCACAG
+EOF
+
+echo "> Check that wrong number of barcodes are detected."
+run_3_dir="$TMPDIR/run_3"
+mkdir -p "$run_3_dir" 
+pushd "$run_3_dir" > /dev/null
+set +eo pipefail
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample1_R1.fastq.gz;$TMPDIR/sample2_R1.fastq.gz" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq.gz;$TMPDIR/sample2_R2.fastq.gz" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/wrong_number_of_barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_gz_*" > /dev/null 2>&1 && echo "Expected non-zero exit code " && exit 1
+set -eo pipefail
+popd > /dev/null
+
+echo "> Check that missing wildcard character is detected."
+run_4_dir="$TMPDIR/run_4"
+mkdir -p "$run_4_dir" 
+pushd "$run_4_dir" > /dev/null
+set +eo pipefail
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample1_R1.fastq.gz;$TMPDIR/sample2_R1.fastq.gz" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq.gz;$TMPDIR/sample2_R2.fastq.gz" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_run4" > /dev/null 2>&1 && echo "Expected non-zero exit code." && exit 1 
+set -eo pipefail
+popd > /dev/null
+
+echo "> Check that a mismatch in the length of the input mates is detected."
+empty_input_file="$TMPDIR/empty.fastq"
+touch "$empty_input_file"
+run_5_dir="$TMPDIR/run_5"
+mkdir -p "$run_5_dir" 
+pushd "$run_5_dir" > /dev/null
+set +eo pipefail
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample1_R1.fastq;$empty_input_file" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq;$TMPDIR/sample2_R2.fastq" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_run5_*" > /dev/null 2>&1 && echo "Expected non-zero exit code " && exit 1
+set -eo pipefail
+popd > /dev/null
+
+echo "> Check that wrong number of input files is detected."
+run_6_dir="$TMPDIR/run_6"
+mkdir -p "$run_6_dir" 
+pushd "$run_6_dir" > /dev/null
+set +eo pipefail
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample1_R1.fastq" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq;$TMPDIR/sample2_R2.fastq" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_run_6_*" > /dev/null 2>&1 && echo "Expected non-zero exit code " && exit 1
+set -eo pipefail
+popd > /dev/null
+
+
+echo "> Check that wrong FASTQ order is detected."
+run_6_dir="$TMPDIR/run_7"
+mkdir -p "$run_6_dir" 
+pushd "$run_6_dir" > /dev/null
+set +eo pipefail
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample2_R1.fastq.gz;$TMPDIR/sample1_R1.fastq.gz" \
+    --input_r2 "$TMPDIR/sample1_R2.fastq;$TMPDIR/sample2_R2.fastq" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_run_6_*" > /dev/null 2>&1 && echo "Expected non-zero exit code " && exit 1
+set -eo pipefail
+popd > /dev/null
+
+
+echo "> Check that order of input FASTQ files must not match with the order of barcodes"
+run_8_dir="$TMPDIR/run_8"
+mkdir -p "$run_8_dir"
+pushd "$run_8_dir" > /dev/null
+"$meta_executable" \
+    --input_r1 "$TMPDIR/sample2_R1.fastq;$TMPDIR/sample1_R1.fastq" \
+    --input_r2 "$TMPDIR/sample2_R2.fastq;$TMPDIR/sample1_R2.fastq" \
+    --genomeDir "$TMPDIR/index/" \
+    --barcodesFasta "$TMPDIR/barcodes.fasta" \
+    --umiLength 10 \
+    --runThreadN 2 \
+    --output "$TMPDIR/output_*" > /dev/null 2>&1 
+popd
+
+echo ">> Check if output directories exists"
+sample1_out="$TMPDIR/output_ACAGTCACAG"
+sample2_out="$TMPDIR/output_CGGGTTTACC"
+assert_directory_exists "$sample1_out"
+assert_directory_exists "$sample2_out"
+
+echo ">> Check if output files have been created"
+for sample in "$sample1_out" "$sample2_out"; do
+  assert_file_exists "$sample/Aligned.sortedByCoord.out.bam" 
+  assert_file_exists "$sample/Unmapped.out.mate1"
+  assert_file_exists "$sample/Unmapped.out.mate2"
+  assert_file_exists "$sample/Log.out"
+  assert_file_exists "$sample/Log.final.out"
+  assert_file_exists "$sample/ReadsPerGene.out.tab"
+done 
+
+
+echo ">> Check if Solo output is present"
+for sample in "$sample1_out" "$sample2_out"; do
+  assert_directory_exists "$sample1_out/Solo.out"
+  assert_directory_exists "$sample1_out/Solo.out/Gene"
+  assert_file_exists "$sample1_out/Solo.out/Barcodes.stats"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/barcodes.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/features.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/raw/matrix.mtx"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/barcodes.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/features.tsv"
+  assert_file_exists "$sample1_out/Solo.out/Gene/filtered/matrix.mtx"
+done
+
+echo ">> Check contents of output"
+echo ">>> Sample 1"
+assert_file_contains "$sample1_out/Solo.out/Barcodes.stats" "nExactMatch              2"
+assert_file_contains "$sample1_out/Log.final.out" "Uniquely mapped reads number |	2"
+assert_file_contains "$sample1_out/Log.final.out" "Number of input reads |	2"
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/barcodes.tsv" || { echo "Barcodes file is different"; exit 1; }
+ACAGTCACAG
+EOF
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/features.tsv" || { echo "Features file is different"; exit 1; }
+gene1	gene1	Gene Expression
+gene2	gene2	Gene Expression
+EOF
+
+cat << EOF | cmp -s "$sample1_out/Solo.out/Gene/filtered/matrix.mtx" || { echo "Matrix file is different"; exit 1; }
+%%MatrixMarket matrix coordinate integer general
+%
+2 1 1
+1 1 1
+EOF
+
+echo ">>> Sample 2"
+assert_file_contains "$sample2_out/Solo.out/Barcodes.stats" "nExactMatch              2"
+assert_file_contains "$sample2_out/Log.final.out" "Uniquely mapped reads number |	2"
+assert_file_contains "$sample2_out/Log.final.out" "Number of input reads |	2"
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/barcodes.tsv" || { echo "Barcodes file is different"; exit 1; }
+CGGGTTTACC
+EOF
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/features.tsv" || { echo "Features file is different"; exit 1; }
+gene1	gene1	Gene Expression
+gene2	gene2	Gene Expression
+EOF
+
+cat << EOF | cmp -s "$sample2_out/Solo.out/Gene/filtered/matrix.mtx" || { echo "Matrix file is different"; exit 1; }
+%%MatrixMarket matrix coordinate integer general
+%
+2 1 1
+2 1 1
+EOF
+
--- a/src/report/OutputSTARsolo.png
+++ b/src/report/OutputSTARsolo.png
--- a/src/report/config.vsh.yaml
+++ b/src/report/config.vsh.yaml
@@ -0,0 +1,77 @@
+name: create_report
+namespace: "report"
+description: |
+  Create a basic QC report in HTML format based on a number of esets.
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ maintainer ]
+  - __merge__: /src/base/authors/marijke_van_moerbeke.yaml
+    roles: [ author, maintainer ]
+argument_groups:
+  - name: "Arguments"
+    arguments:
+    - type: file
+      name: "--eset"
+      required: true
+      multiple: true
+    - type: file
+      name: "--output_report"
+      required: true
+      direction: output
+      example: report.html
+resources:
+  - type: r_script
+    path: script.R
+  - type: r_script
+    path: template.Rmd
+  - type: r_script
+    path: plateLayouts.R
+  - path: OutputSTARsolo.png 
+    type: file
+test_resources:
+  - type: r_script
+    path: test.R
+  - path: ./test_data
+engines:
+  - type: docker
+    image: rocker/r2u:24.04
+    setup:
+      - type: apt
+        packages:
+          - procps
+          - pandoc
+      - type: r
+        script:
+          - install.packages("BiocManager")
+          - BiocManager::install(version = "3.21", type = "source", checkBuilt = TRUE)
+      - type: r
+        bioc:
+          - Biobase
+          - ComplexHeatmap
+        cran:
+          - ggplot2
+          - knitr
+          - gridExtra
+          - RColorBrewer
+          - processx
+          - whisker
+          - rmarkdown
+          - bookdown
+          - data.table
+          - platetools
+          - htmltools
+          - DT
+          - logger
+          - bit64
+        script:
+          - install.packages("oaStyle", repos = c(rdepot = "https://repos.openanalytics.eu/repo/public", getOption("repos")))
+    test_setup:
+      - type: r
+        packages:
+          - testthat
+          - R.utils
+runners:
+  - type: executable
+  - type: nextflow
+
+     
--- a/src/report/plateLayouts.R
+++ b/src/report/plateLayouts.R
@@ -0,0 +1,447 @@
+
+#' Displays the annotation of the wells in a plateLayout
+#' @param plateData a data.table object containing the information
+#' of the plate. This must contain a "WellID".
+#' @param plateName The plate name
+#' @param valueVariable The name of the variable in 'plateData' to
+#' be visualized in a plate layout.
+#' @param textVariable The name of the variable in 'plateData' to be
+#' shown in the wells of the plate layout. If NULL, the valueVariable
+#' is shown.
+#' @param colours A named character vector containing the colours
+#' for the different levels of the valuevariable. The names should
+#' correspond to the dose levels. if not specified, a scheme of blues
+#' will be provided.
+#' @param breaks Numeric vector indicating breaks for plot coloring.
+#' @param colourWellText Colour to display the text in the wells.
+#' @param layout Integer vector of length two with number of rows and
+#' colums in a plate, e.g. \code{c(16,24)}
+#' @param legend.title A title for the legend
+#' @param plot.title A title for the plot, will be contracted
+#' with the plate name
+#' @param ... additional arguments for \code{plateLayout.default} function
+#' @import data.table
+#' @importFrom platetools fill_plate
+#' @export
+plateLayout.annotation <- function(
+  plateData,
+  plateName = character(),
+  valueVariable = "Dose",
+  textVariable = NULL,
+  breaks = NULL, colours = NULL,
+  colourWellText = "black",
+  layout = c(16, 24),
+  legend.title = "Dose",
+  plot.title = "Plate Annotation - ",
+  textFontSize = 9, ...
+) {
+  WellID <- Label <- NULL
+
+  if (!(all(c("WellID", "SampleName") %in% colnames(plateData)))) {
+    stop(" 'WellID' and 'SampleName' column required in plateData object")
+  }
+  
+  #Check WellID Format
+  checkWellID <- grepl("^[[:upper:]]{1,2}[[:digit:]]{1,2}$", plateData$WellID)
+  if(!all(checkWellID)){
+    stop("WellID does not have the correct format")
+  }
+
+
+  plateData[, WellID := paste0(
+    sub(".*([[:alpha:]]).+", "\\1", plateData$WellID),
+    sprintf(
+      "%02d", as.numeric(sub(".*[[:alpha:]](.+)", "\\1", plateData$WellID))
+    )
+  )]
+
+  plateData <- platetools::fill_plate(plateData, "WellID", plate = layout[1]*layout[2])
+
+  plateData$column <- factor(
+    sprintf(
+      "%02d",
+      as.numeric(sub(".*[[:alpha:]](.+)", "\\1", plateData$WellID))
+    ),
+    levels = sprintf("%02d", seq(1, layout[2]))
+  )
+  plateData$row <- factor(sub(".*([[:alpha:]]).+", "\\1", plateData$WellID),
+                          levels = LETTERS[seq(1, layout[1])])
+
+  if (!is.null(valueVariable)){
+    plateData[, values := as.character(plateData[, ..valueVariable][[1]])]
+    valueVar <- "values"
+  }else{
+    plateData[, values := "grey"]
+    valueVar <- "values"
+    colours <- setNames("grey", "grey")
+  }
+
+
+  if (is.null(colours)) {
+
+    blues <- colorRampPalette(c("#d6e0ff", "#2171B5"))
+    greens <- colorRampPalette(c("light green", "dark green"))
+
+    numLevels <- sort(as.numeric(as.character(unique(plateData[, values])[
+      grepl(
+        "^[[:digit:]]+([.][[:digit:]]+)?$",
+        trimws(unique(plateData[, values]))
+      )
+    ])))
+    otherLevels <- sort(as.character(unique(plateData[, values])[
+      !grepl(
+        "^[[:digit:]]+([.][[:digit:]]+)?$",
+        trimws(unique(plateData[,values]))
+      )
+    ]))
+
+    colours <- c(blues(length(numLevels)), greens(length(otherLevels)), "red")
+    names(colours) <- c(numLevels, otherLevels, "failed")
+  }
+
+  if (!is.null(textVariable)) {
+    plateData[,
+      Label :=  do.call(paste, c(.SD, sep = "\n ")),
+      .SDcols = textVariable
+    ]
+    plateData[, Label :=  gsub("-", "-\n", Label)]
+    plateData[, Label :=  gsub("_", "_\n", Label)]
+    textVar <- "Label"
+  } else {
+    textVar <- NULL
+  }
+
+
+  if (is.null(breaks)){
+    breaks <- seq_len(length(colours))
+  }
+
+  plateLayout(
+    plateData = plateData, valueVariable = valueVar,
+    textVariable = textVar, plateName = plateName,
+    breaks = breaks, colourWellText = colourWellText,
+    legend.title = legend.title, layout = layout,
+    colours = colours, plot.title = plot.title,
+    textFontSize = textFontSize, ...
+  )
+}
+
+
+
+#' Create a heatmap of values in a plateLayout view. The values can be
+#' library sizes, number of genes, qcScore (0/1) or a factor.
+#' @param plateData A data.table of the values to be visualized with
+#' at least the column of interest (specified in 'varOfInterest')
+#' and a 'WellID' column indicating the wells in the plate. The WellID
+#' is a combination of a letter (row in the plate) and an integer
+#' (column in the plate).
+#' @param valueVariable The name of the variable in 'plateData'
+#' to be visualized in a plate layout
+#' @param textVariable The name of the variable in 'plateData'
+#' to be shown in the wells of the plate layout. Defaults to the
+#' valueVariable and if NULL, no text will be displayed.
+#' @param breaks Numeric vector indicating breaks for plot coloring.
+#' @param colours Colours to be used for levels specified by
+#' the breaks. If NULL, a colour scheme of purples is shown.
+#' @param colourWellText Colour to display the text in the wells.
+#' @param layout Integer vector of length two with number of rows
+#' and colums in a plate, e.g. \code{c(16,24)}
+#' @param makeContourColours Logical, whether or not the plate
+#' layout will contain a contour colours for the wells based on the
+#' parameters in 'contourColours' and 'categories'
+#' @param contourVariable The variable used for the contour colouring
+#' @param contourColours Character vector specifying a colour for
+#' each range in 'categories'
+#' @param labelsCategories Character vector specifying the names
+#' (labels) for each range in 'categories'
+#' @param categories if contour Variable is not a factor, a numeric
+#' vector specifying the categories to divide the 'varOfInterest',
+#' including the lower and upper limits.
+#' @param plateName The plate name
+#' @param plot.title A title for the plot, will be contracted with
+#' the plate name
+#' @param legend.title A title for the legend
+#' @param displayHeatmap Logical, whether to display the plateLayout heatmap
+#' @param saveHeatmap Logical, whether to save the plateLayout heatmap
+#' @param outputDir The directory where the plateLayout heatmap should be saved
+#' @param prefix The prefix to the file name of the saved plateLayout heatmap
+#' @param ... additional arguments for \code{ComplexHeatmap::Heatmap} function
+#' @importFrom platetools fill_plate
+#' @importFrom RColorBrewer brewer.pal
+#' @importFrom ComplexHeatmap Heatmap
+#' @importFrom circlize colorRamp2
+#' @importFrom grid grid.text grid.rect gpar legendGrob	gpar
+#' @importFrom grDevices dev.off png
+#' @importFrom graphics title
+#' @export
+plateLayout <- function(
+  plateData, valueVariable, textVariable = valueVariable,
+  breaks = NULL, colours = NULL, colourWellText = "white", textFontSize = 6,
+  layout = c(16, 24), makeContourColours = FALSE, contourVariable = character(),
+  contourColours = c("red", "orange", "seagreen3"),lwdContours = c(1, 1, 1),
+  labelsCategories = c('1', '2', '3'), categories = NULL, plateName = character(),
+  plot.title = character(), legend.title = NULL, legendFontSize = 15,
+  row_split = rep("A", 16), col_split = rep("A", 24), legendFontSizeTitle = 15,
+  displayHeatmap = TRUE, saveHeatmap = FALSE, outputDir = ".", prefix = ""
+) {
+  WellID <- NULL
+  if (!(all(c("WellID", "SampleName") %in% colnames(plateData)))) {
+    stop(" 'WellID' and 'SampleName' column required in plateData object")
+  }
+
+
+  plateData[, WellID := paste0(
+    sub(".*([[:alpha:]]).+", "\\1", plateData$WellID),
+    sprintf(
+      "%02d",
+      as.numeric(sub(".*[[:alpha:]](.+)", "\\1", plateData$WellID))
+    )
+  )]
+
+  plateData <- platetools::fill_plate(plateData, "WellID", plate = 384)
+
+  plateData$column <- factor(
+    sprintf("%02d", as.numeric(
+      sub(".*[[:alpha:]](.+)", "\\1", plateData$WellID)
+    )),
+    levels = sprintf("%02d", seq(1, layout[2]))
+  )
+  plateData$row <- factor(sub(".*([[:alpha:]]).+", "\\1", plateData$WellID),
+                          levels = LETTERS[seq(1, layout[1])])
+
+
+  plateValues <- plateLayoutFormat(
+    plateData,
+    varOfInterest = valueVariable,
+    rows = layout[1],
+    cols = layout[2]
+  )
+  if (!is.null(textVariable)) {
+    plateText <- plateLayoutFormat(
+      plateData, varOfInterest = textVariable,
+      rows = layout[1],
+      cols = layout[2]
+    )
+  }
+  plot.title <- gsub(
+    "^([a-z])", "\\U\\1",
+    gsub("([A-Z])", " \\1",
+    plot.title, perl = TRUE), perl = TRUE
+  )
+  mainTitle <- paste0(plot.title, plateName)
+  plateContourColours <- matrix("", nrow = layout[1], ncol = layout[2])
+
+  if (makeContourColours) {
+    contourData <- plateData[WellType %in% c("nonEmpty", "Treated Wells"), ]
+
+    if (is.numeric(contourData[, ..contourVariable][[1]])) {
+      contourData$contours <- cut(
+        contourData[, ..contourVariable][[1]],
+        categories, left = TRUE,
+        right = TRUE,
+        labels = labelsCategories)
+    }
+    else {
+      contourData$contours <- contourData[, ..contourVariable][[1]]
+    }
+    names(contourColours) <- labelsCategories
+    names(lwdContours) <- labelsCategories
+    for (i in seq_len(layout[1])) {
+      for (j in seq_len(layout[2])) {
+        tryCatch({
+          sampleHit <- which(
+            as.character(contourData$WellID) == paste0(
+              LETTERS[i], sprintf("%02d", j)
+            )
+          )
+          if (length(sampleHit) == 1) {
+            plateContourColours[i, j] <- as.character(
+              contourData[sampleHit,'contours'][[1]]
+            )
+          }
+        },
+        error = function(e) {
+          print(paste0(LETTERS[i], sprintf("%02d", j), " is missing."))
+        }
+        )
+      }
+    }
+  }
+
+  plateValues$contours <- plateContourColours
+  colnames(plateValues$values) <- seq_len(ncol(plateValues$values))
+
+  if (is.null(breaks)) {
+    breakValues <- plateValues$values
+    breakValues[which(is.na(breakValues))] <- 0
+    if (all(breakValues >= 0)) {
+      breaks <- computeBreaks(7, max(plateValues$values, na.rm = TRUE))
+    } else {
+      breaks <- quantile(plateValues$values,  probs = seq(0, 1, 0.125))
+    }
+  }
+
+  if (is.null(colours)) {
+    colours <- tryCatch({
+      circlize::colorRamp2(
+        breaks = breaks,
+        colors = brewer.pal(length(breaks), "Purples")
+      )
+    },
+    error = function(cond){
+      
+      message("Recomputed breaks for proper colour mapping")
+      
+      breakValues <- plateValues$values
+      breakValues[which(is.na(breakValues))] <- 0
+      if (all(breakValues >= 0)) {
+        breaks <- computeBreaks(7, max(plateValues$values, na.rm = TRUE))
+      } else {
+        breaks <- quantile(plateValues$values,  probs = seq(0, 1, 0.125))
+      }
+      
+      circlize::colorRamp2(
+        breaks = breaks,
+        colors = brewer.pal(length(breaks), "Purples")
+      )
+      
+    })
+  }
+  
+  ht <- Heatmap(
+    plateValues$values,
+    column_title = mainTitle, column_title_side = "top",
+    rect_gp = gpar(lwd = 0.4),
+    cluster_rows = FALSE, cluster_columns = FALSE,
+    col = colours, row_title = NULL,
+    row_split = row_split, column_split = col_split,
+    row_names_side = "left",
+    cluster_row_slices = FALSE,
+    cluster_column_slices = FALSE,
+    show_heatmap_legend = TRUE,
+    heatmap_legend_param = list(
+      title = ifelse(
+        is.null(legend.title),
+        paste0(valueVariable, "\n"),
+        paste0(legend.title, "\n")
+      ),
+      grid_height = unit(9, "mm"), border = "black",
+      labels_gp = gpar(fontsize = legendFontSize),
+      title_gp = gpar(fontsize = legendFontSizeTitle)
+    ),
+    cell_fun = function(j, i, x, y, width, height, fill) {
+      if (is.na(plateValues$values[i, j])) {
+        grid.rect(
+          x, y, width, height,
+          gp = gpar(fill = "white", alpha = 0.7, lwd = 0.7, col = "white")
+        )
+      }
+      else if (!is.null(textVariable)) {
+        grid.text(
+          plateText$values[i, j], x, y,
+          just = "centre",
+          gp = gpar(fontsize = textFontSize, col = colourWellText)
+        )
+      }
+      if (makeContourColours) {
+        if (!is.na(plateValues$contours[i, j])) {
+          grid.rect(
+            x, y, width, height,
+            gp = gpar(
+              col = contourColours[as.character(plateValues$contours[i, j])],
+              fill = NA,
+              lwd = lwdContours[as.character(plateValues$contours[i, j])]
+            )
+          )
+        }
+      }
+    }
+  )
+
+  if (displayHeatmap) {
+    print(ht)
+  }
+  if (saveHeatmap) {
+    png(
+      file.path(
+        outputDir,
+        paste0(prefix,gsub(" |-", "",plot.title), "_", plateName, ".png")
+      ),
+      width = 30, height = 10, units = "cm", res = 1200
+    )
+    print(ht)
+    dev.off()
+  }
+
+  return(ht)
+}
+
+
+#' Return numerical matrix with number of reads that corresponds to the
+#' plate layout
+#' @param data A data.frame of the values to be visualized with at least
+#' the columnof interest (specified in 'varOfInterest') and a 'WellID' column
+#' indicating the wells in the plate. The WellID is a combination of a
+#' letter (row in the plate) and an integer (column in the plate).
+#' @param varOfInterest The name of the variable in 'data' to be visualized
+#' in a plate layout
+#' @param rows number of rows in a plate layout
+#' @param cols number of columns in a plate layout
+#' @param verbose if \code{TRUE}, samples missing from the plate
+#' will be reported
+#' @export
+plateLayoutFormat <- function(
+  data, varOfInterest,
+  rows = 16, cols = 24,
+  verbose = FALSE
+) {
+  plateValues <- matrix(NA, nrow = rows, ncol = cols)
+  for (i in seq_len(rows)) {
+    for (j in seq_len(cols)) {
+      tryCatch({
+        sampleHit <- which(
+          as.character(data$WellID) == paste0(LETTERS[i], sprintf("%02d", j))
+        )
+        if(length(sampleHit) == 1){
+          plateValues[i, j] <- data[sampleHit, ..varOfInterest][[1]]
+        }
+      },
+      error = function(e) {
+        if (verbose == TRUE) {
+          print(paste0(LETTERS[i], sprintf("%02d", j), " is missing."))
+        }
+      }
+      )
+    }
+  }
+
+  row.names(plateValues) <- LETTERS[1:rows]
+  return(list("values" = plateValues))
+}
+
+
+
+#' Helper function to automate break selection for raw count data
+#'
+#' This function creates an exponentially increasing vector for given number
+#' breaks between zero and some element of choice. It is particularly useful for
+#' raw counts or raw counts per million.
+#'
+#' @param nBreaks Number of breaks to be generated
+#' @param maxElement Maximum value of data entries
+#' @export
+computeBreaks <- function(nBreaks, variable) {
+
+  maxElement <- max(variable, na.rm = TRUE)
+  if (length(unique(variable)) == 1) {
+    breaks <-  c(0, 0.5, ifelse(maxElement < 1, 1,  maxElement))
+  } else {
+    coefSystem <- solve(
+      rbind(c(1, 1), c(1, (nBreaks - 1)))) %*% c(0, log(maxElement)
+    )
+    coefExp <- c(exp(coefSystem[1]), coefSystem[2])
+    breaks <- coefExp[1] * exp((1:(nBreaks - 1)) * coefExp[2])
+    breaks <- unique(c(0, breaks))
+  }
+  return(breaks)
+}
--- a/src/report/script.R
+++ b/src/report/script.R
@@ -0,0 +1,33 @@
+library(whisker)
+library(logger)
+
+log_info("Setting temporary directory to: {meta$temp_dir}")
+Sys.setenv(TMP = meta$temp_dir)
+temp_folder <- tempdir(check = TRUE)
+log_info("Created temporary directory {temp_folder}")
+
+template <- file.path(meta$resources_dir, "template.Rmd")
+
+esets_normalized <- lapply(par$eset, function(eset_path) {
+  return(file.path(normalizePath(dirname(eset_path)), basename(eset_path)))
+})
+
+log_info(paste0(
+  "Rendering markdown {template} to HTML ",
+  "{par$output_report} with esets {paste(esets_normalized, collapse = ', ')}"
+))
+
+rmarkdown::render(
+  normalizePath(template),
+  output_file = basename(par$output_report),
+  output_dir = dirname(par$output_report),
+  runtime = "static",
+  intermediates_dir = par$report_dir,
+  clean = TRUE,
+  params = list(
+    esets = esets_normalized,
+    outputDir = par$report_dir
+  )
+)
+
+log_info("Done")
--- a/src/report/template.Rmd
+++ b/src/report/template.Rmd
@@ -0,0 +1,977 @@
+---
+title: "Exploratory Data Report"
+date: "`r format(Sys.time(), '%d %B, %Y')`"
+editor_options:
+  chunk_output_type: console
+output: 
+  oaStyle::html_report
+# parameters which are overwritten by the script
+params:
+  outputDir: 'output/'
+  esets:
+    - sample1.rds
+    - sample2.rds
+---
+
+<!---
+Copy this template in your working directory (where you want to run the report).
+This template can be used as a starting document to run a preliminary DRUGseq report
+-->
+
+<!---
+Use full page width
+-->
+
+<style type="text/css">
+div.main-container {
+  max-width: 1600px !important;
+  margin-left: auto;
+  margin-right: auto;
+}
+</style>
+
+
+
+```{r params, eval = TRUE, include = FALSE}
+outputDir <- params$outputDir
+esets <- params$esets
+```
+
+
+```{r outputDir, echo = FALSE}
+## Required: ABSOLUTE outputDir
+outputDir <- file.path(outputDir)
+
+# When working on a windows computer it should be
+# "/Users/..." instead of "C:/Users/..."
+if (.Platform$OS.type == "windows") {
+  outputDir <- paste0(
+    "/",
+    paste(
+      unlist(strsplit(outputDir, split = "/"))[-1], collapse = "/"
+    ),
+    "/"
+  )
+}
+```
+
+
+
+
+```{r optionsChunkDoNotModify, echo = FALSE, message = FALSE, warning=FALSE}
+
+## Chunk with options for knitr. This chunk should not be modified.
+knitr::opts_chunk$set(
+  eval = TRUE,
+  echo = FALSE, 
+  message = FALSE,
+  cache = FALSE,
+  warning = FALSE,
+  error = FALSE,
+  comment = NA, #"#",
+  tidy = FALSE,
+  collapse = TRUE,
+  out.width = "100%",
+  fig.width = 20,
+  fig.height = 10,
+  results = "asis")
+
+knitr::opts_knit$set(root.dir = getwd())
+
+options(warn = 1, width = 200)
+
+```
+
+```{r libraries_and_functions}
+source("plateLayouts.R")
+library(ComplexHeatmap)
+library(data.table)
+library(ggplot2)
+library(knitr)
+library(Biobase)
+library(gridExtra)
+library(RColorBrewer)
+```
+
+
+```{r dataImport}
+
+# Create esetList
+esetList <- sapply(
+  esets, simplify = FALSE,
+  USE.NAMES = TRUE,
+  function(eset_raw) {
+    if (!file.exists(eset_raw)) {
+      stop(paste0("Provided path '", eset_raw, "' is not a file."))
+    }
+    eset <- readRDS(eset_raw)
+  }
+)
+pools <- sapply(esetList, function(eset) {
+  unique(eset$PoolName)
+})
+names(esetList) <- unlist(pools)
+
+# Create qcData
+pDataList <- lapply(esetList, function(eset) data.table(pData(eset)))
+qcData <- rbindlist(pDataList, fill = TRUE)
+
+textVars <- "SampleName"
+annotationVar <- "PoolName"
+
+if (!"SampleName" %in% names(qcData)) {
+  qcData[, SampleName := paste0(PoolName, "_", WellBC)]
+}
+qcData[, log10LibSize := round(log10(NumberOfInputReads))]
+qcData[, (annotationVar) := lapply(.SD, as.factor), .SDcols = annotationVar]
+
+
+colourList <- list()
+Design_levels <- sort(
+  as.character(unique(qcData[, ..annotationVar][[1]])),
+  decreasing = TRUE
+)
+
+if (length(Design_levels) == 1) {
+  colours <- c("#d6e0ff", "lightgrey")
+  names(colours) <- c(Design_levels, "Empty")
+    colourList[[annotationVar]] <- list(
+      "colours" = colours,
+      "annotVar" = annotationVar,
+      "text" = textVars
+    )
+}else if (length(Design_levels) == 2) {
+  colours <- c("#d6e0ff", "#FF9999")
+
+  names(colours) <- c(Design_levels)
+  colourList[[annotationVar]] <- list(
+    "colours" = colours,
+    "annotVar" = annotationVar,
+    "text" = textVars
+  )
+} else if (length(Design_levels) <= 20) {
+
+  if (length(Design_levels) > 12) {
+    colours <- c(
+      brewer.pal(12, "Set3"),
+      brewer.pal((length(Design_levels) - 12),
+      "Pastel2")
+    )
+  } else {
+    colours <- c(brewer.pal(length(Design_levels), "Set3"))
+  }
+
+  names(colours) <- c(Design_levels)
+  colourList[[annotationVar]] <- list(
+    "colours" = colours,
+    "annotVar" = annotationVar,
+    "text" = textVars
+  )
+} else {
+  colours <- c("#d6e0ff")
+  names(colours) <- c("nonEmpty")
+  colourList[[annotVar]] <- list(
+    "colours" = colours,
+    "annotVar" = annotVar,
+    "text" = annotVar
+  )
+}
+```
+
+# Pool Description
+
+Per pool within this study, there are several pool layout plots shown, based on the
+
+* number of STAR input reads (= library size)
+
+* log10 transformed number of STAR input reads
+
+* number of detected UMIs
+
+* number of detected genes
+
+* number of chromosomal reads 
+
+* percentage of ERCC 
+
+* percentage of mitochondria 
+
+
+> The values for the different samples within each pool is expected to be comparable if the content of the different pools is equally diverse.
+
+```{r plateAnnotation, out.width = "100%",fig.width = 20, fig.height= 10}
+
+plateVars <- c("NumberOfInputReads", "log10LibSize", "NumberOfMappedReads",
+               "NumberOfChromReads", "NumberOfUMIs", "NumberOfGenes",
+               "pctMT", "pctERCC")
+
+breaksVars <- lapply(
+  plateVars,
+  function(var) {
+    computeBreaks(7, qcData[, ..var])
+  }
+)
+names(breaksVars) <- plateVars
+
+for (pool in pools){
+  cat("\n\n")
+  cat(paste0("## ", pool, " {.tabset} \n\n"))
+  poolData <- qcData[PoolName == pool]
+  lapply(plateVars, function(plateVar) {
+    cat("\n\n")
+    cat(sprintf("### %s {.unnumbered}", plateVar))
+    cat("\n\n")
+    plateLayout(
+      poolData, valueVariable = plateVar,
+      textFontSize = 10, legendFontSize = 12,
+      plateName = pool, plot.title = "libSize - ",
+      legend.title = "libSize", breaks = breaksVars[[plateVar]]
+    )
+    cat("\n\n")
+  })
+  cat("\n\n")
+}
+```
+
+<br>
+
+
+# Data Distributions
+
+
+## Reads Distributions {.tabset}
+
+The 4 box plots below represent the distributions per pool of the different samples based on:
+
+* the number of STAR input reads
+
+* the number of STAR mapped reads
+
+* the percentage of STAR mapped reads
+
+* the number of detected genes
+
+> The distributions contribute to the QC metrics mentioned in Par 3. The higher these values, the better.
+> The data range for the different plates is expected to be comparable if the content of the different plates is equally diverse.
+
+
+### Number of Input Reads {.tabset .unnumbered}
+
+```{r settings_1}
+
+nColPlots = 1
+figHeight = 7
+
+```
+
+#### Distribution {.tabset .unnumbered}
+
+
+```{r boxplots_input_plate, fig.height = figHeight}
+ggplot(
+  qcData,
+  aes(
+    x = PoolName,
+    y = NumberOfInputReads, colour = PoolName
+  )
+) + geom_boxplot() + ylab("Number of Input Reads") +
+  ggtitle("Number of Input Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+
+```
+
+
+### Number of Mapped Reads {.tabset .unnumbered}
+
+#### Distribution {.unnumbered}
+
+```{r boxplots_mapped_plate, fig.height = figHeight}
+
+ggplot(
+  qcData,
+  aes(x = PoolName, y = NumberOfMappedReads, colour = PoolName)
+) + geom_boxplot() + ylab("Number of Mapped Reads") +
+  ggtitle("Number of Mapped Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+
+#### pct Mapped Reads {.unnumbered}
+
+```{r boxplots_pctMapped_plate, fig.height = figHeight}
+ggplot(
+  qcData,
+  aes(x = PoolName, y = PctMappedReads, colour = PoolName)
+) +
+  geom_boxplot() +
+  ylab("pct Mapped Reads") +
+  ggtitle("pct Mapped Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+### Number of Chromosomal Reads {.tabset .unnumbered}
+
+#### Distribution {.unnumbered}
+
+```{r boxplots_chrom_plate, fig.height = figHeight}
+
+ggplot(
+  qcData,
+  aes(x = PoolName, y = NumberOfChromReads, colour = PoolName)
+) + geom_boxplot() + ylab("Number of Chromosomal Reads") +
+  ggtitle("Number of Chromosomal Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+
+```
+
+#### pct Chromosomal Reads {.unnumbered}
+
+```{r boxplots_pctChrom_plate, fig.height = figHeight}
+
+ggplot(
+  qcData,
+  aes(x = PoolName, y = pctChrom, colour = PoolName)
+) + geom_boxplot() + ylab("pct Chromosomal Reads") +
+  ggtitle("pct Chromosomal Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+### Number of UMIs {.tabset .unnumbered}
+
+#### Distribution {.tabset .unnumbered}
+
+
+```{r boxplots_umi_plate, fig.height = figHeight}
+
+ggplot(
+  qcData,
+  aes(x = PoolName, y = NumberOfUMIs, colour = PoolName)
+) + geom_boxplot() + ylab("Number of UMIs") +
+  ggtitle('Number of UMIs') +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+
+```
+
+#### Density distribution {.unnumbered}
+
+```{r density_numberOfUMIs}
+
+## Pre-filtering data exploration
+dt_plot <- melt(
+  qcData,
+  id.vars = c("SampleName", "PoolName", "WellID"),
+  measure.vars = c("NumberOfInputReads", "NumberOfMappedReads", "NumberOfUMIs")
+)
+
+readsDensity_plot <- ggplot(dt_plot, aes(value))
+readsDensity_plot <- readsDensity_plot +
+  geom_density(aes(fill = variable), alpha=0.8) +
+  facet_grid(~ PoolName, scales = "free_x", space = "fixed", drop = TRUE) +
+  geom_vline(
+    xintercept = 5e5,
+    linetype = "dashed",
+    color = "steelblue3", size = 2
+  ) +
+  annotate(
+    "text",
+    x = 3.5e5, y = 2e-6, label = "500k",
+    angle = 90, color = "steelblue3", size = 10
+  ) +
+  geom_vline(
+    xintercept = 1.5e6, linetype = "dashed",
+    color = "forestgreen", size = 2
+  ) +
+  annotate(
+    "text", x = 1.35e6, y = 2e-6, label = "1.5M",
+    angle = 90, color = "forestgreen", size = 10
+  ) +
+  labs(
+    title = "Density plot",
+    subtitle = paste0(
+      "# Samples with NumberOfMappedReads > 1.5M: ",
+      length(which(qcData$NumberOfMappedReads > 1.5e6)),
+      "\n# Samples with NumberOfUMIs > 500k: ",
+      length(which(qcData$NumberOfUMIs > 5e5))
+    ),
+    caption = paste0("# Total samples (after removing empty): ", nrow(qcData)),
+    x = "Count",
+    fill = "Variable"
+  ) +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 5),
+    axis.text.x = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    plot.subtitle = element_text(size = 17),
+    plot.caption = element_text(size = 15),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15),
+    axis.text.y = element_blank(),
+    axis.ticks.y = element_blank(),
+    axis.title.y = element_blank()
+  )
+readsDensity_plot
+
+```
+
+### Number of Genes {.tabset .unnumbered}
+
+#### Distribution {.unnumbered}
+
+```{r boxplots_genes_plate, fig.height = figHeight}
+ggplot(
+  qcData,
+  aes(x = PoolName, y = NumberOfGenes, colour = PoolName)
+) +
+  geom_boxplot() + ylab("Number of Genes") + 
+  ggtitle("Number of Genes") + 
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+## {.tabset .toc-ignore .unnumbered}
+
+
+In addition, several plots are shown visualizing the efficiency of the reads-to-genes translation:
+
+* the number of input reads vs the number of mapped reads
+
+* the number of chromosomal reads vs the number of mapped reads
+
+* the number of mapped reads per UMI vs the number of mapped reads 
+
+* the number of UNI vs the number of mapped reads
+
+* the number of mapped reads vs the number of genes
+
+* the number of chromosomal reads vs the number of genes
+
+* the number of mapped reads per UMI vs the number of genes 
+
+### Mapping Efficiency {.tabset .unnumbered}
+
+#### Number of Input Reads {.unnumbered}
+
+```{r mapping_efficiency_1_plate, fig.height = 7}
+
+ggplot(
+  qcData,
+  aes(x = NumberOfInputReads, y = NumberOfMappedReads, colour = PoolName)
+) + 
+  geom_point() +
+  xlab("Number of Input Reads") +
+  ylab("Number of Mapped Reads") +
+  ggtitle("Number of Mapped Reads vs Number of Input Reads") + 
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+  )
+
+```
+
+
+#### Number of Chromosomal Reads {.unnumbered}
+
+```{r mapping_efficiency_2_plate, fig.height = 7}
+
+ggplot(
+  qcData,
+  aes(x = NumberOfChromReads, y = NumberOfMappedReads, colour = PoolName)
+) + geom_point() +
+  xlab("Number of Chromosomal Reads") + ylab("Number of Mapped Reads") +
+  ggtitle("Number of Chromosomal Reads vs Number of Mapped Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+)
+
+```
+
+
+#### Number of UMI {.unnumbered}
+
+```{r mapping_efficiency_4_plate, fig.height = 7}
+
+ggplot(
+  qcData,
+  aes(x =NumberOfUMIs, y =  NumberOfMappedReads, colour = PoolName)
+) + geom_point() +
+  ylab("Number of Mapped Reads") + xlab("Number of UMIs ") +
+  ggtitle("Number of UMIs vs Number of Mapped Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+  )
+
+```
+
+### Counting Efficiency {.tabset .unnumbered}
+
+#### Number of Mapped Reads {.unnumbered}
+
+```{r gene_efficiency_1_plate, fig.height = 7} 
+ggplot(
+  qcData,
+  aes(x = NumberOfMappedReads, y = NumberOfGenes, colour = PoolName)
+) + geom_point() +
+  ylab("Number of Genes") + xlab("Number of Mapped Reads") +
+  ggtitle("Number of Genes vs Number of Mapped Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+  )
+```
+
+#### Number of Chromosomal Reads {.unnumbered}
+
+```{r gene_efficiency_2_plate, fig.height = 7} 
+ggplot(
+  qcData,
+  aes(x = NumberOfChromReads, y = NumberOfGenes, colour = PoolName)
+) + geom_point() +
+  ylab("Number of Genes") + xlab("Number of Chromosomal Reads") +
+  ggtitle("Number of Genes vs Number of Chromosomal Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+  )
+```
+
+
+
+## Sequencing Saturation {.tabset}
+
+The barplots below represent the sequencing saturation per sample as determined by STAR, split per pool. 
+The HT-RNAseq platform aims for shallow sequencing resulting in relatively low sequencing saturations of 10-20%.
+In addition, the sequencing saturation vs the number of input reads is shown.
+
+### Sequencing Saturation {.unnumbered}
+
+
+
+```{r sequencingSaturation, fig.height = figHeight}
+
+ggplot(
+  qcData,
+  aes(x = WellID, y = SequencingSaturation, fill = PoolName)
+) + geom_bar(stat = "identity", position = "dodge") +
+  xlab("Samples") + ggtitle("Sequencing Saturation per Sample") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(1, "lines"),
+    text = element_text(size = 10),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.text.y = element_text(size = 15),
+    axis.ticks.x = element_blank()
+  )
+```
+
+### Sequencing Saturation - Input Reads {.unnumbered}
+
+
+```{r sequencingSaturation_inputReads, fig.height = figHeight}
+
+
+ggplot(
+  qcData,
+  aes(x = NumberOfInputReads, y = SequencingSaturation, colour = PoolName)
+) + geom_point() +
+  ggtitle("Sequencing Saturation vs Number of Input Reads") +
+  theme(strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+  )
+```
+
+### Sequencing Saturation  - Mapped Reads {.unnumbered}
+
+```{r sequencingSaturation_mappedReads, fig.height = figHeight}
+ggplot(
+  qcData,
+  aes(x = NumberOfChromReads, y = SequencingSaturation, colour = PoolName)
+) + geom_point() +
+  ggtitle("Sequencing Saturation vs Number of Chromosomal Reads") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size=10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size=18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+)
+```
+
+<br>
+
+## Genomic Origin {.tabset} 
+
+The 3 boxplots below represent, per pool, the distributions of the percentage of reads mapping to:
+
+* chromosomal regions
+
+* mitochondrial regions
+
+* ERCC spike-ins
+
+The 4th plot summarises the above results across samples per pool.
+
+The 5th plot shows the percentage of reads mapped to the transcriptome (as counted by STAR).  This  measurement serves as a proxy for the percentage of reads mapped to exons.
+
+> The percentage ERCC contributes to the QC metrics mentioned in Par 3. This value is ideally as low as possible (but non-zero to ensure the they have been spiked in) and comparable for the different pools.
+
+
+
+
+### pctChrom {.tabset .unnumbered}
+
+
+```{r genomicOrigin_chrom_plate, fig.height = figHeight}
+
+ggplot(
+  qcData, aes(x = PoolName, y = pctChrom, colour = PoolName)
+) +
+  geom_boxplot() +
+  ggtitle("pctChrom") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+
+### pctMT {.tabset .unnumbered}
+
+```{r genomicOrigin_mt_plate, fig.height = figHeight}
+
+ggplot(
+  qcData,
+  aes(x = PoolName, y = pctMT, colour = PoolName)
+) +
+  geom_boxplot() + ggtitle("pctMT") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+### pctERCC {.tabset .unnumbered}
+
+
+```{r genomicOrigin_ercc_plate, fig.height = figHeight}
+ggplot(qcData, aes(x = PoolName, y = pctERCC, colour = PoolName))  +
+  geom_boxplot() +
+  ggtitle("pctERCC") +
+  theme(
+    strip.text.x = element_text(size = 20),
+    panel.spacing = unit(2, "lines"),
+    text = element_text(size = 10),
+    axis.text.y = element_text(angle = 90, size = 14),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title.y = element_text(size = 15),
+    axis.text.x = element_blank(),
+    axis.ticks.x = element_blank()
+  )
+```
+
+
+### Genomic Summary {.tabset .unnumbered}
+
+
+
+```{r genomicOrigin_summary_plate}
+meanPctChromMTData <- qcData[, .(
+  "pctChrom" = median(pctChrom),
+  "pctMT" = median(pctMT),
+  "pctERCC" = median(pctERCC)
+), by = PoolName]
+meanPctChromMTDataLong <- melt(
+  meanPctChromMTData,
+  id.vars = "PoolName",
+  measure.vars = c("pctChrom", "pctMT", "pctERCC"),
+  variable.name = "Origin", value.name = "pct"
+)
+ggplot(
+  meanPctChromMTDataLong,
+  aes(fill = Origin, y = pct, x = PoolName)) +
+  geom_bar(position = "stack", stat = "identity") +
+  ggtitle("Genomic Origin") +
+  theme(
+    text = element_text(size = 10),
+    axis.text = element_text(angle = 90, size = 15),
+    plot.title = element_text(size = 18),
+    legend.text = element_text(size = 15),
+    legend.title = element_text(size = 17),
+    axis.title = element_text(size = 15)
+  )
+
+```
+
+
+
+# Depletion {.tabset}   
+
+<div align="center">
+```{r depletion}
+
+
+for (eset_name in pools) {
+  cat("\n\n")
+  cat(paste0("## ", eset_name, " {.unnumbered}"))
+  cat("\n\n")
+
+  eset <- esetList[[eset_name]]
+  average_reads <- sort(apply(exprs(eset), 1, mean), decreasing = TRUE)
+  plotData <- data.table(
+    ENSGID = names(average_reads),
+    av_count = average_reads
+  )
+
+  gen_descript <- data.table(
+    ENSGID = eset@featureData@data$gene_id,
+    Description = eset@featureData@data$GENENAME
+  )
+  order_gen_descript <- gen_descript[
+    match(plotData$ENSGID, gen_descript$ENSGID),
+  ]
+
+  g <- ggplot(
+    plotData[c(1:100)],
+    aes(x = reorder(ENSGID, -av_count), y = av_count)
+  ) + geom_bar(stat = "identity") +
+    theme(
+      axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 12),
+      axis.text.y = element_text(size = 12),
+      legend.text = element_text(size = 15),
+      legend.title = element_text(size = 15),
+      axis.title = element_text(size = 18),
+      plot.title = element_text(size = 20)
+    ) + ylab("Average Counts") + xlab("Genes")
+
+  print(g)
+
+  cat("\n\n")
+  cat("<br>")
+  cat("<br>")
+
+  print(htmltools::tagList((DT::datatable(order_gen_descript[1:100, ]))))
+}
+```
+</div>
+
+
+<br>
+<br>
+<br>
+<br>
+
+# Glossary {.unnumbered}
+
+
+## Read {.unlisted .unnumbered}
+
+A read is a oligonucleotide (a short RNA fragment) that has been sequenced. It consists of a fixed number of base pairs (bp) and therefore has a specific read length.
+
+
+
+## Input Read {.unlisted .unnumbered}
+
+Each read of the fastq file used as input to the STAR aligner is considered an input read.
+
+
+
+## Read With Valid Barcode {.unlisted .unnumbered}
+
+A read with a valid barcode is a read for which the barcode matches the white list of barcodes under the given restriction of the number of allowed mismatches. The number of reads with a valid barcode is lower or equal to the number of input reads.
+
+
+
+## Mapped Read {.unlisted .unnumbered}
+
+A read that has been aligned against the reference genome and for which one or more suitable matching locations have been found is a mapped read. Depending on the number of allowed mismatches this might or might not be be an exact match. The number of mapped reads is lower or equal to the number of reads with a valid barcode.
+
+
+
+## Uniquely Mapped Read {.unlisted .unnumbered}
+
+A read for which one and only one suitable matching location in the reference genome was found is an uniquely mapped read. The number of uniquely mapped reads is lower or equal to the number of mapped reads.
+
+
+
+## Counted Read {.unlisted .unnumbered}
+
+A mapped read will only be counted if it overlaps (1 nucleotide or more) with one and only one gene. The number of counted reads is lower or equal to the number of (uniquely) mapped reads.
+
+
+
+## UMIs {.unlisted .unnumbered}
+
+Unique molecular identifiers (UMI) are short sequences in order to uniquely tag each molecule in a sample library. Sequencing with UMIs allows bioinformatics software to filter out duplicate reads and PCR errors with a high level of accuracy and report unique reads.
+
+The reported UMIs is the number of UMIs among the set of reads that map to an unique gene, i.e the number of reads is deduplicated.
+
+
+
+## pctERCC {.unlisted .unnumbered}
+
+The percentage of reads mapping to the ERCC genes among the total number of **mapped** reads.
+
+
+
+## pctMT {.unlisted .unnumbered}
+
+The percentage of reads mapping to the MT genes among the total number of **mapped** reads.
+
+
+
+## Sequencing Saturation {.unlisted .unnumbered}
+
+The sequencing saturation is a measure of the fraction of library complexity. The inverse of one minus the sequencing saturation can be interpreted as the number of additional reads it would take to detect a new transcript. Consequently, a low sequencing saturation indicates a shallow sequencing in which a new transcript could be discovered with a few reads.
+
+<br>
+<br>
+<br>
+<br>
+
+<center>
+![](OutputSTARsolo.png)
+</center>
+
+<br>
+<br>
--- a/src/report/test.R
+++ b/src/report/test.R
@@ -0,0 +1,41 @@
+library(whisker)
+library(testthat)
+library(R.utils)
+
+cat(">> Creating temporary directory \n")
+Sys.setenv(TMP = meta$temp_dir)
+temp_folder <- tempdir(check = TRUE)
+
+cat(">> Running component create_report for test case \n")
+
+input_dir <- file.path(meta$resources_dir, "test_data")
+stopifnot(file.exists(input_dir))
+
+
+out <- processx::run(meta$executable, c(
+  "--eset", file.path(meta$resources_dir, "test_data", "eset.sample_one.rds"),
+  "--eset", file.path(meta$resources_dir, "test_data", "eset.sample_two.rds"),
+  "--output_report", "report.html"
+))
+
+expect_equal(out$status, 0)
+expect_true(file.exists("report.html"))
+
+cat(">>  Test succesful \n")
+
+cat(">> Running component create_report with symbolic links \n")
+
+link_sample_1 <- file.path(temp_folder, "eset.sample_one.rds")
+link_sample_2 <- file.path(temp_folder, "eset.sample_two.rds")
+createLink(link = link_sample_1,
+           target = file.path(meta$resources_dir, "test_data", "eset.sample_one.rds"))
+createLink(link = link_sample_2,
+           target = file.path(meta$resources_dir, "test_data", "eset.sample_two.rds"))
+
+out <- processx::run(meta$executable, c(
+  "--eset", link_sample_1,
+  "--eset", link_sample_2,
+  "--output_report", "report2.html"
+))
+
+expect_true(file.exists("report2.html"))
--- a/src/report/test_data/eset.sample_one.rds
+++ b/src/report/test_data/eset.sample_one.rds
--- a/src/report/test_data/eset.sample_two.rds
+++ b/src/report/test_data/eset.sample_two.rds
--- a/src/stats/combine_star_logs/config.vsh.yaml
+++ b/src/stats/combine_star_logs/config.vsh.yaml
@@ -0,0 +1,72 @@
+name: combine_star_logs
+namespace: "stats"
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ author, maintainer ]
+argument_groups:
+  - name: "Arguments"
+    arguments:
+      - name: "--barcodes"
+        type: string
+        multiple: true
+        required: true
+        description: |
+          Barcodes responding to the respective log files.
+      - name: "--star_logs"
+        type: file
+        multiple: true
+        required: true
+        description: |
+          Paths to the STAR log files (most frequently called Log.final.out)
+        direction: input
+        example: "Log.final.out"
+      - name: "--gene_summary_logs"
+        direction: input
+        type: file
+        multiple: true
+        required: true
+        description: |
+          Paths to the Summary.csv files from the STAR Solo output. Can be found in
+          the 'Solo.out/Gene' folder relative to the root of the STAR output directory. 
+        example: "Summary.txt"
+      - name: "--reads_per_gene_logs"
+        direction: input
+        type: file
+        multiple: true
+        required: true
+        description: |
+          Paths to the 'ReadsPerGene.out.tab' files as output by STAR.
+      - name: "--output"
+        type: file
+        direction: output
+        default: "starLogs.txt"
+        description: |
+          Tab-delimited file describing for each barcode (as the rows), the metrics (as columns)
+          gathered from the different input files. 
+      
+resources:
+- type: python_script
+  path: script.py
+
+test_resources:
+  - type: python_script
+    path: test.py
+  - path: test_data
+
+engines:
+  - type: docker
+    image: python:3.12-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - pandas
+    test_setup:
+      - type: python
+        packages:
+          - viashpy
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/stats/combine_star_logs/script.py
+++ b/src/stats/combine_star_logs/script.py
@@ -0,0 +1,228 @@
+import logging
+import pandas as pd
+from itertools import batched, starmap
+
+### VIASH START
+meta = {
+    "name": "combine_star_logs",
+}
+par = {
+    "star_logs": ["src/stats/combine_star_logs/test_data/barcode_1/Log.final.out",
+                  "src/stats/combine_star_logs/test_data/barcode_2/Log.final.out"],
+    "gene_summary_logs": ["src/stats/combine_star_logs/test_data/barcode_1/summary.csv",
+                          "src/stats/combine_star_logs/test_data/barcode_2/summary.csv"], 
+    "reads_per_gene_logs": ["src/stats/combine_star_logs/test_data/barcode_1/ReadsPerGene.out.tab",
+                            "src/stats/combine_star_logs/test_data/barcode_2/ReadsPerGene.out.tab"],
+    "output": "output.txt",
+    "barcodes": ["ACGG", "TTTT"],
+}
+
+### VIASH END
+
+logger = logging.getLogger()
+console_handler = logging.StreamHandler()
+logger.addHandler(console_handler)
+logger.setLevel(logging.DEBUG)
+
+
+def handle_percentages(column_value):
+    # TODO: handle this more gracefully
+    if column_value:
+        return column_value.strip('%')
+    return column_value
+
+def star_log_to_dataframe(barcode: str, log_path) -> pd.DataFrame:
+    logger.info("Reading STAR log %s for barcode '%s'", log_path, barcode)
+    result = pd.read_table(log_path, sep=r"\|\t+", converters={"Value": handle_percentages},
+                           engine="python", header=None, skip_blank_lines=True,
+                           skipinitialspace=True, names=["Category", "Value"], index_col=0,
+                           skiprows=[0, 1, 2])
+    logger.info("Read %d row(s) and %d column(s) from STAR logs at %s", 
+                *result.shape, log_path)
+    return result
+
+
+def summary_to_dataframe(barcode: str, summary_path) -> pd.DataFrame:
+    logger.info("Reading summary log %s for barcode %s", summary_path, barcode)
+    result = pd.read_table(summary_path, sep=",",
+                           header=None, names=["Category", "Value"],
+                           index_col=0, dtype=pd.StringDtype())
+    logger.info("Read %d row(s) and %d column(s) from summary file at %s",
+                *result.shape, summary_path)
+    return result
+
+
+def reads_per_gene_to_dataframe(barcode, read_per_gene_path) -> pd.DataFrame:
+    logger.info("Reading reads per gene file %s for barcode %s", read_per_gene_path, barcode)
+    result = pd.read_table(read_per_gene_path, skiprows=[0, 1, 2, 3], header=None, sep="\t",
+                           dtype={"geneID": pd.StringDtype(),
+                                  "Unstranded": pd.Int64Dtype(),
+                                  "posStrand": pd.Int64Dtype(),
+                                  "negStrand": pd.Int64Dtype()},
+                           index_col=0, names=["geneID", "Unstranded", "posStrand", "negStrand"])
+    result = result[["Unstranded"]] # Do not use .loc here because we need a DataFrame, not a Series
+    df = pd.DataFrame({"Value": result.sum()})
+    df = df.rename({"Unstranded": "NumberOfCountedReads"}, errors="raise")
+    df.index.name = "Category"
+    logger.info("Read %d row(s) and %d column(s) from reads per gene file at %s",
+                *df.shape, read_per_gene_path)
+    return df
+
+def star_log_remove_unwanted_entries_and_adjust_format(barcode, df: pd.DataFrame) -> pd.DataFrame:
+    """
+    For a single star log (Log.final.out) in dataframe format, filter out the
+    entries that are not needed and format the labels for some metrics:
+        - Replace '%' with 'pect' in the labels.
+        - Remove labels ending with ':' 
+          (mostly the section separators like 'MULTI-MAPPING READS:' and 'UNMAPPED READS:')
+        - Remove the metrics we do no need based on the following keywords:
+          Mapping speed, Average, Number of splices, per base, chimeric reads, average
+    
+    The dataframe provided as input must have an index with 1 level with the metric names.
+    """
+    # Remove index values ending with ':' (rows like 'MULTI-MAPPING READS:','UNIQUE READS:')
+    logger.info("Filtering STAR logs for barcode %s. Starting with %d row(s) and %d column(s)", barcode, *df.shape)
+    to_keep = ~df.index.to_series().str.endswith(":")
+    # Remove index values where the values contain any of these substrings
+    regex_columns_to_remove = "Mapping speed|Average|Number of splices|per base|chimeric reads|average"
+    to_keep = to_keep & ~df.index.to_series().str.contains(regex_columns_to_remove, regex=True)
+    logger.info("Removed the following log entries for barcode '%s':\n\t%s",
+                barcode,
+                "\n\t".join(to_keep[~to_keep].index.to_list()))
+    result = df.loc[to_keep]
+
+    # Replace % by pect, remove columns, use camel case and remove spaces
+    # You might be tempted to use .title() to make everything uppercase,
+    # but characters which are already uppercase should stay that way.
+    # (example: NumberOfUMIs and not NumberOfUmis)
+    result.index = result.index.str.replace("%", "pect")\
+                    .str.replace(":", "")\
+                    .str.replace(r"(?:^|\s).", lambda m:m.group(0).upper(), regex=True)\
+                    .str.replace(" ", "")
+    result = result.rename({"UniquelyMappedReadsNumber": "NumberOfMappedReads", 
+                            "UniquelyMappedReadsPect": "PctMappedReads"}, errors="raise")
+    logger.info("Done filtering STAR logs for barcode %s. Result has %d row(s) and %d column(s). "
+                "Found entries:\n\t%s", 
+                barcode, *result.shape, "\n\t".join(result.index.to_list()))
+    return result
+
+
+def summary_remove_unwanted_entries_and_adjust_format(barcode, df: pd.DataFrame) -> pd.DataFrame:
+    logger.info("Filtering and formatting summary logs for barcode %s. "
+                "Starting with %d row(s) and %d column(s)", barcode, *df.shape)
+    columns_to_remove = (
+        "Number of Reads",
+        "Q30 Bases in RNA read",
+        "Reads Mapped to Genome: Unique",
+        "Reads Mapped to Transcriptome: Unique Genes",
+        "Reads in Cells Mapped to Unique Genes",
+        "Median UMI per Cell",
+        "Median Genes per Cell",
+        "Reads Mapped to Genome: Unique+Multiple",
+        "Median Reads per Cell",
+        "Mean UMI per Cell",
+        "Mean Genes per Cell",
+    )
+
+    to_keep = ~df.index.isin(columns_to_remove)
+    logger.info("Removed the following summary entries for barcode '%s':\n\t%s",
+                barcode,
+                "\n\t".join(df.loc[~to_keep].index.to_list()))
+    result = df.loc[to_keep]
+    result.index = result.index.str.replace(r"(?:^|\s).", lambda m:m.group(0).upper(),
+                                            regex=True).str.replace(" ", "")
+    to_rename = {"UMIsInCells": "NumberOfUMIs", 
+                 "TotalGenesDetected": "NumberOfGenes"}
+    try:
+        result = result.rename(to_rename, errors="raise")
+    except KeyError as e:
+        raise KeyError(f"Tried to rename log entries ({','.join(to_rename)}) in the summary "
+                       f"log for barcode {barcode}, but an entry was not found in the file. "
+                       "Make sure that you are using the correct version of STAR."
+                       f"Available entries: {", ".join(result.index.to_list())}") from e
+    logger.info("Done filtering summary logs for barcode %s. Result has %d row(s) and %d column(s). "
+                "Found entries:\n\t%s",
+                barcode, *result.shape, "\n\t".join(result.index.to_list()))
+    return result
+
+
+def join_dfs(df_list, barcodes) -> pd.DataFrame:
+    # Combine the dataframes together and add the barcodes as a level to the dataframe
+    # in order to make a 2-level index (first level the barcodes and second level the metrics).
+    result = pd.concat(dict(zip(barcodes, df_list)), names=["WellBC"])
+    # Pivot the table by moving the metrics to the columns. Its added as an extra level, 
+    # so we can just frop the 'Values' level that was already there
+    result = result.unstack(level="Category").droplevel(0, axis="columns")
+    return result
+
+def main(par):
+    logger.info("Component started.")
+    # Provide an overview of the parameters in the logs
+    parameters_str = [f'\t{param}: {param_val}\n' for param, param_val in par.items()]
+    logger.info("Parameters:\n%s", "".join(parameters_str).rstrip())
+    star_logs, gene_summary_logs, reads_per_gene_logs, barcodes  = par["star_logs"], \
+        par["gene_summary_logs"], par["reads_per_gene_logs"], par["barcodes"]
+    number_of_inputs = tuple(len(i) for i in (star_logs, gene_summary_logs,
+                                              reads_per_gene_logs, barcodes))
+    if len(set(number_of_inputs)) != 1:
+        raise ValueError("Expected the same number of inputs for 'star_logs' (%d), "
+                         "'gene_summary_logs' (%d), 'reads_per_gene_logs' (%d) "
+                         "and 'barcodes' (%d)." % number_of_inputs)
+    
+    logs_to_process = [
+        (star_log_to_dataframe, star_log_remove_unwanted_entries_and_adjust_format, star_logs),
+        (summary_to_dataframe, summary_remove_unwanted_entries_and_adjust_format, gene_summary_logs),
+        (reads_per_gene_to_dataframe, None, reads_per_gene_logs),
+    ]
+    logger.info("Formatting the contents of the log files.") 
+    all_logs_data = []
+    for df_generator, formatter, data in logs_to_process:
+        data_as_df = list(starmap(df_generator, zip(barcodes, data)))
+        data_formatted = data_as_df
+        if formatter:
+            data_formatted = list(starmap(formatter, zip(barcodes, data_as_df)))
+        data_joined = join_dfs(data_formatted, barcodes)
+        all_logs_data.append(data_joined)
+
+    logger.info("Joining entries across the different logs together.") 
+    all_stats = pd.concat(all_logs_data, axis=1)
+    logger.info("Log statistics were gathered for the following barcodes: %s", 
+                ", ".join(all_stats.index.to_list()))
+    dtypes = {
+        'NumberOfInputReads': pd.UInt64Dtype(),
+        'NumberOfMappedReads': pd.UInt64Dtype(),
+        'PctMappedReads': pd.Float64Dtype(),
+        'NumberOfReadsMappedToMultipleLoci': pd.UInt64Dtype(),
+        'PectOfReadsMappedToMultipleLoci':  pd.Float64Dtype(), 
+        'NumberOfReadsMappedToTooManyLoci': pd.UInt64Dtype(),
+        'PectOfReadsMappedToTooManyLoci':  pd.Float64Dtype(),
+        'NumberOfReadsUnmappedTooManyMismatches': pd.UInt64Dtype(),
+        'PectOfReadsUnmappedTooManyMismatches':  pd.Float64Dtype(),
+        'NumberOfReadsUnmappedTooShort': pd.UInt64Dtype(), 
+        'PectOfReadsUnmappedTooShort':  pd.Float64Dtype(),
+        'NumberOfReadsUnmappedOther': pd.UInt64Dtype(),
+        'PectOfReadsUnmappedOther': pd.Float64Dtype(),
+        'ReadsWithValidBarcodes': pd.Float64Dtype(),
+        'SequencingSaturation': pd.Float64Dtype(),
+        'Q30BasesInCB+UMI': pd.Float64Dtype(),
+        'ReadsMappedToTranscriptome:Unique+MultipeGenes': pd.Float64Dtype(),
+        'EstimatedNumberOfCells': pd.UInt64Dtype(),
+        'FractionOfReadsInCells': pd.Float64Dtype(),
+        'MeanReadsPerCell': pd.UInt64Dtype(),
+        'NumberOfUMIs': pd.UInt64Dtype(),
+        'NumberOfGenes': pd.UInt64Dtype(),
+        'NumberOfCountedReads': pd.UInt64Dtype(),
+    }
+    all_stats = all_stats.astype(dtypes) 
+    # batched() is used here to print a limited amount of columnns at a time
+    # to make sure that they are all displayed (pandas might limit the view for readability)
+    logger.info("Summary of final output:\n%s\n",
+                "\n".join(repr(all_stats.loc[:,columns].describe())
+                          for columns in batched(all_stats.columns, 3))) 
+    logger.info("Writing output to %s", par["output"])
+    all_stats.reset_index("WellBC").to_csv(par["output"], sep="\t", header=True,
+                                           index=False, float_format='%g')
+    logger.info("Finished %s.", meta["name"])
+
+if __name__ == "__main__":
+    main(par)
--- a/src/stats/combine_star_logs/test.py
+++ b/src/stats/combine_star_logs/test.py
@@ -0,0 +1,182 @@
+import pytest
+import sys
+import re
+import pandas as pd
+from pathlib import Path
+from uuid import uuid4
+from subprocess import CalledProcessError
+
+### VIASH START
+meta = {
+    "resources_dir": "./src/stats/combine_star_logs/",
+    "executable": "target/executable/stats/combine_star_logs/combine_star_logs",
+    "config": "src/stats/combine_star_logs/config.vsh.yaml"
+}
+### VIASH END
+
+@pytest.fixture
+def test_resources_path():
+    return Path(meta["resources_dir"]) / "test_data"
+
+@pytest.fixture
+def barcode_1_star_log(test_resources_path):
+    return test_resources_path / "barcode_1" / "Log.final.out"
+
+@pytest.fixture
+def barcode_1_reads_per_gene_file(test_resources_path):
+    return test_resources_path / "barcode_1" / "ReadsPerGene.out.tab"
+
+@pytest.fixture
+def barcode_1_summary(test_resources_path):
+    return test_resources_path / "barcode_1" / "summary.csv"
+
+@pytest.fixture
+def barcode_2_star_log(test_resources_path):
+    return test_resources_path / "barcode_2" / "Log.final.out"
+
+@pytest.fixture
+def barcode_2_reads_per_gene_file(test_resources_path):
+    return test_resources_path / "barcode_2" / "ReadsPerGene.out.tab"
+
+@pytest.fixture
+def barcode_2_summary(test_resources_path):
+    return test_resources_path / "barcode_2" / "summary.csv"
+
+@pytest.fixture
+def no_reads_mapped_star_log(test_resources_path):
+    return test_resources_path / "empty" / "Log.final.out"
+
+@pytest.fixture
+def no_reads_mapped_reads_per_gene_file(test_resources_path):
+    return test_resources_path / "empty" / "ReadsPerGene.out.tab"
+
+@pytest.fixture
+def no_reads_mapped_summary(test_resources_path):
+    return test_resources_path / "empty" / "summary.csv"
+
+@pytest.fixture
+def random_path(tmp_path):
+    def wrapper(extension=None):
+        extension = "" if not extension else f".{extension}"
+        return tmp_path / f"{uuid4()}{extension}"
+    return wrapper 
+
+def test_incorrect_number_of_inputs_raises(run_component,
+                                           barcode_1_star_log, barcode_2_star_log,
+                                           barcode_1_reads_per_gene_file, barcode_2_reads_per_gene_file,
+                                           barcode_1_summary, barcode_2_summary,
+                                           random_path):
+    output_path = random_path("txt")
+    with pytest.raises(CalledProcessError) as err:
+        run_component([
+            "--barcodes", "foo;bar",
+            "--star_logs", f"{barcode_1_star_log}", 
+            "--reads_per_gene_logs", f"{barcode_1_reads_per_gene_file};{barcode_2_reads_per_gene_file}",
+            "--gene_summary_logs", f"{barcode_1_summary};{barcode_2_summary}",
+            "--output", output_path,
+        ])
+    assert re.search(r"ValueError: Expected the same number of inputs for 'star_logs' \(1\), "
+                     r"'gene_summary_logs' \(2\), 'reads_per_gene_logs' \(2\) and 'barcodes' \(2\)\.",
+            err.value.stdout.decode('utf-8'))
+
+
+
+def test_equal_number_of_argument(run_component,
+                                  barcode_1_star_log, barcode_2_star_log,
+                                  barcode_1_reads_per_gene_file, barcode_2_reads_per_gene_file,
+                                  barcode_1_summary, barcode_2_summary,
+                                  random_path):
+    output_path = random_path("txt")
+    run_component([
+        "--barcodes", "foo;bar",
+        "--star_logs", f"{barcode_1_star_log};{barcode_2_star_log}", 
+        "--reads_per_gene_logs", f"{barcode_1_reads_per_gene_file};{barcode_2_reads_per_gene_file}",
+        "--gene_summary_logs", f"{barcode_1_summary};{barcode_2_summary}",
+        "--output", output_path,
+    ])
+    # We use strings here to make a comparison of the file contents without
+    # doing any inferences of the numerical data type (i.e. exact file contents).
+    expected_dict = {
+        'NumberOfInputReads': ["96398", "10155"], 
+        'NumberOfMappedReads': ["70824", "7179"], 
+        'PctMappedReads': ["73.47", "70.69"], 
+        'NumberOfReadsMappedToMultipleLoci': ["0", "0"], 
+        'PectOfReadsMappedToMultipleLoci': ["0", "0"], 
+        'NumberOfReadsMappedToTooManyLoci': ["22281", "2248"],
+        'PectOfReadsMappedToTooManyLoci': ["23.11", "22.14"],
+        'NumberOfReadsUnmappedTooManyMismatches': ["0", "0"], 
+        'PectOfReadsUnmappedTooManyMismatches': ["0", "0"], 
+        'NumberOfReadsUnmappedTooShort': ["2697", "553"], 
+        'PectOfReadsUnmappedTooShort': ["2.8", "5.45"], 
+        'NumberOfReadsUnmappedOther': ["596", "175"], 
+        'PectOfReadsUnmappedOther': ["0.62", "1.72"], 
+        'ReadsWithValidBarcodes': ["0.999782", "0.999803"],
+        'SequencingSaturation': ["0.0602963", "0.0539344"], 
+        'Q30BasesInCB+UMI': ["0.980096", "0.984461"],
+        'ReadsMappedToTranscriptome:Unique+MultipeGenes': ["0.60411", "0.530871"],
+        'EstimatedNumberOfCells': ["1", "1"],
+        'FractionOfReadsInCells': ["1", "1"],
+        'MeanReadsPerCell': ["53602", "4969"],
+        'NumberOfUMIs': ["50370", "4701"], 
+        'NumberOfGenes': ["8767", "2397"],
+        'NumberOfCountedReads': ["17", "15"],
+    }
+    expected = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    expected.index = pd.Index(["foo", "bar"], name="WellBC", dtype=pd.StringDtype())
+    assert output_path.is_file()
+
+    contents = pd.read_csv(output_path, sep="\t", index_col=0, dtype=pd.StringDtype())
+    assert set(("NumberOfInputReads", "SequencingSaturation",
+                "NumberOfGenes", "NumberOfUMIs", "NumberOfCountedReads",
+                "PctMappedReads")).issubset(set(contents.columns))
+    pd.testing.assert_frame_equal(contents, expected)
+
+def test_empty(run_component, no_reads_mapped_star_log,
+               no_reads_mapped_reads_per_gene_file, no_reads_mapped_summary,
+               random_path):
+    """
+    Sometimes the summary.csv contains '-nan' values, make sure they
+    are properly handled.
+    """
+    output_path = random_path("txt")
+    run_component([
+        "--barcodes", "foo",
+        "--star_logs", no_reads_mapped_star_log,
+        "--reads_per_gene_logs", no_reads_mapped_reads_per_gene_file,
+        "--gene_summary_logs", no_reads_mapped_summary,
+        "--output", output_path,
+    ])
+    expected_dict = {
+        'NumberOfInputReads': ["1327"],
+        'NumberOfMappedReads': ["116"],
+        'PctMappedReads': ["8.74"],
+        'NumberOfReadsMappedToMultipleLoci': ["0"],
+        'PectOfReadsMappedToMultipleLoci': ["0"],
+        'NumberOfReadsMappedToTooManyLoci': ["43"],
+        'PectOfReadsMappedToTooManyLoci': ["3.24"],
+        'NumberOfReadsUnmappedTooManyMismatches': ["0"],
+        'PectOfReadsUnmappedTooManyMismatches': ["0"],
+        'NumberOfReadsUnmappedTooShort': ["1166"],
+        'PectOfReadsUnmappedTooShort': ["87.87"],
+        'NumberOfReadsUnmappedOther': ["2"],
+        'PectOfReadsUnmappedOther': ["0.15"],
+        'ReadsWithValidBarcodes': ["0.023361"],
+        'SequencingSaturation': [pd.NA],
+        'Q30BasesInCB+UMI': ["0.917408"],
+        'ReadsMappedToTranscriptome:Unique+MultipeGenes': ["0"],
+        'EstimatedNumberOfCells': ["0"],
+        'FractionOfReadsInCells': [pd.NA],
+        'MeanReadsPerCell': ["0"],
+        'NumberOfUMIs': ["0"],
+        'NumberOfGenes': ["0"],
+        'NumberOfCountedReads': ["0"],
+    }
+    expected = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    expected.index = pd.Index(["foo"], name="WellBC", dtype=pd.StringDtype())
+    contents = pd.read_csv(output_path, sep="\t", index_col=0, dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(contents, expected)
+
+
+
+if __name__ == '__main__':
+    sys.exit(pytest.main([__file__]))
--- a/src/stats/combine_star_logs/test_data/barcode_1/Log.final.out
+++ b/src/stats/combine_star_logs/test_data/barcode_1/Log.final.out
@@ -0,0 +1,37 @@
+                                 Started job on |	Jun 26 09:38:11
+                             Started mapping on |	Jun 26 09:38:14
+                                    Finished on |	Jun 26 09:38:23
+       Mapping speed, Million of reads per hour |	38.56
+
+                          Number of input reads |	96398
+                      Average input read length |	57
+                                    UNIQUE READS:
+                   Uniquely mapped reads number |	70824
+                        Uniquely mapped reads % |	73.47%
+                          Average mapped length |	56.93
+                       Number of splices: Total |	6432
+            Number of splices: Annotated (sjdb) |	6285
+                       Number of splices: GT/AG |	6331
+                       Number of splices: GC/AG |	33
+                       Number of splices: AT/AC |	2
+               Number of splices: Non-canonical |	66
+                      Mismatch rate per base, % |	0.61%
+                         Deletion rate per base |	0.01%
+                        Deletion average length |	1.38
+                        Insertion rate per base |	0.00%
+                       Insertion average length |	1.24
+                             MULTI-MAPPING READS:
+        Number of reads mapped to multiple loci |	0
+             % of reads mapped to multiple loci |	0.00%
+        Number of reads mapped to too many loci |	22281
+             % of reads mapped to too many loci |	23.11%
+                                  UNMAPPED READS:
+  Number of reads unmapped: too many mismatches |	0
+       % of reads unmapped: too many mismatches |	0.00%
+            Number of reads unmapped: too short |	2697
+                 % of reads unmapped: too short |	2.80%
+                Number of reads unmapped: other |	596
+                     % of reads unmapped: other |	0.62%
+                                  CHIMERIC READS:
+                       Number of chimeric reads |	0
+                            % of chimeric reads |	0.00%
--- a/src/stats/combine_star_logs/test_data/barcode_1/ReadsPerGene.out.tab
+++ b/src/stats/combine_star_logs/test_data/barcode_1/ReadsPerGene.out.tab
@@ -0,0 +1,8 @@
+N_unmapped	11111	22222	33333
+N_multimapping	0	0	0
+N_noFeature	44444	55555	66666
+N_ambiguous	77777	88888	99999
+gene1	2	0	0
+gene2	0	0	0
+gene3	6	0	6
+gene5	9	6	3
--- a/src/stats/combine_star_logs/test_data/barcode_1/summary.csv
+++ b/src/stats/combine_star_logs/test_data/barcode_1/summary.csv
@@ -0,0 +1,20 @@
+Number of Reads,96398
+Reads With Valid Barcodes,0.999782
+Sequencing Saturation,0.0602963
+Q30 Bases in CB+UMI,0.980096
+Q30 Bases in RNA read,0.799904
+Reads Mapped to Genome: Unique+Multiple,0.734704
+Reads Mapped to Genome: Unique,0.734704
+Reads Mapped to Transcriptome: Unique+Multipe Genes,0.60411
+Reads Mapped to Transcriptome: Unique Genes,0.556049
+Estimated Number of Cells,1
+Reads in Cells Mapped to Unique Genes,53602
+Fraction of Reads in Cells,1
+Mean Reads per Cell,53602
+Median Reads per Cell,53602
+UMIs in Cells,50370
+Mean UMI per Cell,50370
+Median UMI per Cell,50370
+Mean Genes per Cell,8767
+Median Genes per Cell,8767
+Total Genes Detected,8767
--- a/src/stats/combine_star_logs/test_data/barcode_2/Log.final.out
+++ b/src/stats/combine_star_logs/test_data/barcode_2/Log.final.out
@@ -0,0 +1,37 @@
+                                 Started job on |	Jun 26 09:38:56
+                             Started mapping on |	Jun 26 09:39:00
+                                    Finished on |	Jun 26 09:39:02
+       Mapping speed, Million of reads per hour |	18.28
+
+                          Number of input reads |	10155
+                      Average input read length |	57
+                                    UNIQUE READS:
+                   Uniquely mapped reads number |	7179
+                        Uniquely mapped reads % |	70.69%
+                          Average mapped length |	56.36
+                       Number of splices: Total |	526
+            Number of splices: Annotated (sjdb) |	495
+                       Number of splices: GT/AG |	502
+                       Number of splices: GC/AG |	4
+                       Number of splices: AT/AC |	1
+               Number of splices: Non-canonical |	19
+                      Mismatch rate per base, % |	0.85%
+                         Deletion rate per base |	0.00%
+                        Deletion average length |	1.09
+                        Insertion rate per base |	0.00%
+                       Insertion average length |	1.07
+                             MULTI-MAPPING READS:
+        Number of reads mapped to multiple loci |	0
+             % of reads mapped to multiple loci |	0.00%
+        Number of reads mapped to too many loci |	2248
+             % of reads mapped to too many loci |	22.14%
+                                  UNMAPPED READS:
+  Number of reads unmapped: too many mismatches |	0
+       % of reads unmapped: too many mismatches |	0.00%
+            Number of reads unmapped: too short |	553
+                 % of reads unmapped: too short |	5.45%
+                Number of reads unmapped: other |	175
+                     % of reads unmapped: other |	1.72%
+                                  CHIMERIC READS:
+                       Number of chimeric reads |	0
+                            % of chimeric reads |	0.00%
--- a/src/stats/combine_star_logs/test_data/barcode_2/ReadsPerGene.out.tab
+++ b/src/stats/combine_star_logs/test_data/barcode_2/ReadsPerGene.out.tab
@@ -0,0 +1,8 @@
+N_unmapped	101010	202020	303030
+N_multimapping	0	0	0
+N_noFeature	404040	505050	606060
+N_ambiguous	707070	808080	909090
+gene1	0	0	0
+gene2	0	0	0
+gene6	5	5	0
+gene4	10	2	8
--- a/src/stats/combine_star_logs/test_data/barcode_2/summary.csv
+++ b/src/stats/combine_star_logs/test_data/barcode_2/summary.csv
@@ -0,0 +1,20 @@
+Number of Reads,10155
+Reads With Valid Barcodes,0.999803
+Sequencing Saturation,0.0539344
+Q30 Bases in CB+UMI,0.984461
+Q30 Bases in RNA read,0.786064
+Reads Mapped to Genome: Unique+Multiple,0.706942
+Reads Mapped to Genome: Unique,0.706942
+Reads Mapped to Transcriptome: Unique+Multipe Genes,0.530871
+Reads Mapped to Transcriptome: Unique Genes,0.489316
+Estimated Number of Cells,1
+Reads in Cells Mapped to Unique Genes,4969
+Fraction of Reads in Cells,1
+Mean Reads per Cell,4969
+Median Reads per Cell,4969
+UMIs in Cells,4701
+Mean UMI per Cell,4701
+Median UMI per Cell,4701
+Mean Genes per Cell,2397
+Median Genes per Cell,2397
+Total Genes Detected,2397
--- a/src/stats/combine_star_logs/test_data/empty/Log.final.out
+++ b/src/stats/combine_star_logs/test_data/empty/Log.final.out
@@ -0,0 +1,37 @@
+                                 Started job on |	Jun 26 09:38:56
+                             Started mapping on |	Jun 26 09:39:00
+                                    Finished on |	Jun 26 09:39:02
+       Mapping speed, Million of reads per hour |	18.28
+
+                          Number of input reads |	1327
+                      Average input read length |	58
+                                    UNIQUE READS:
+                   Uniquely mapped reads number |	116
+                        Uniquely mapped reads % |	8.74%
+                          Average mapped length |	54.11
+                       Number of splices: Total |	6
+            Number of splices: Annotated (sjdb) |	4
+                       Number of splices: GT/AG |	4
+                       Number of splices: GC/AG |	0
+                       Number of splices: AT/AC |	0
+               Number of splices: Non-canonical |	2
+                      Mismatch rate per base, % |	6.63%
+                         Deletion rate per base |	0.13%
+                        Deletion average length |	2.00
+                        Insertion rate per base |	0.00%
+                       Insertion average length |	0.00
+                             MULTI-MAPPING READS:
+        Number of reads mapped to multiple loci |	0
+             % of reads mapped to multiple loci |	0.00%
+        Number of reads mapped to too many loci |	43
+             % of reads mapped to too many loci |	3.24%
+                                  UNMAPPED READS:
+  Number of reads unmapped: too many mismatches |	0
+       % of reads unmapped: too many mismatches |	0.00%
+            Number of reads unmapped: too short |	1166
+                 % of reads unmapped: too short |	87.87%
+                Number of reads unmapped: other |	2
+                     % of reads unmapped: other |	0.15%
+                                  CHIMERIC READS:
+                       Number of chimeric reads |	0
+                            % of chimeric reads |	0.00%
--- a/src/stats/combine_star_logs/test_data/empty/ReadsPerGene.out.tab
+++ b/src/stats/combine_star_logs/test_data/empty/ReadsPerGene.out.tab
@@ -0,0 +1,8 @@
+N_unmapped	1211	1211	1211
+N_multimapping	0	0	0
+N_noFeature	23	26	109
+N_ambiguous	6	2	0
+gene1	0	0	0
+gene2	0	0	0
+gene6	0	0	0
+gene4	0	0	0
--- a/src/stats/combine_star_logs/test_data/empty/summary.csv
+++ b/src/stats/combine_star_logs/test_data/empty/summary.csv
@@ -0,0 +1,20 @@
+Number of Reads,1327
+Reads With Valid Barcodes,0.023361
+Sequencing Saturation,-nan
+Q30 Bases in CB+UMI,0.917408
+Q30 Bases in RNA read,0.711711
+Reads Mapped to Genome: Unique+Multiple,0.0874152
+Reads Mapped to Genome: Unique,0.0874152
+Reads Mapped to Transcriptome: Unique+Multipe Genes,0
+Reads Mapped to Transcriptome: Unique Genes,0
+Estimated Number of Cells,0
+Reads in Cells Mapped to Unique Genes,0
+Fraction of Reads in Cells,-nan
+Mean Reads per Cell,0
+Median Reads per Cell,0
+UMIs in Cells,0
+Mean UMI per Cell,0
+Median UMI per Cell,0
+Mean Genes per Cell,0
+Median Genes per Cell,0
+Total Genes Detected,0
--- a/src/stats/generate_pool_statistics/config.vsh.yaml
+++ b/src/stats/generate_pool_statistics/config.vsh.yaml
@@ -0,0 +1,56 @@
+name: generate_pool_statistics
+namespace: "stats"
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ author, maintainer ]
+  - __merge__: /src/base/authors/marijke_van_moerbeke.yaml
+    roles: [ contributor ]
+argument_groups:
+  - name: "Arguments"
+    arguments:
+      - name: "--nrReadsNrGenesPerChrom"
+        type: file
+        multiple: true
+        description: |
+          Path to an output file that contains a .tsv formatted table describing
+          per chromosome the number of reads that were mapped to that chromosome (NumberOfReads
+          column) and the number of genes on that chromosome that had at least one
+          read mapped to it (NumberOfGenes).
+        direction: input
+        default: [processedBamFile_well1.tsv, processedBamfile_well2.tsv]
+      - name: "--nrReadsNrGenesPerChromPool"
+        direction: output
+        type: file
+        multiple: false
+        description: |
+          Pivot table in tsv format of the combined input nrReadsNrGenesPerChrom files. Describes
+          per chromosome (as columns) the number of reads, as well as the total number 
+          of reads per cell barcode and the percentage of nuclear, ERCC and mitochondrial
+          reads.
+        example: "nrReadsNrGenesPerChrom.txt"
+
+resources:
+- type: python_script
+  path: script.py
+
+test_resources:
+  - type: python_script
+    path: test.py
+
+engines:
+  - type: docker
+    image: python:3.12-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - pandas
+    test_setup:
+      - type: python
+        packages:
+          - viashpy
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/stats/generate_pool_statistics/script.py
+++ b/src/stats/generate_pool_statistics/script.py
@@ -0,0 +1,94 @@
+import pandas as pd
+from pathlib import Path
+import re
+
+### VIASH START
+par = {
+    "nrReadsNrGenesPerChrom": ["src/stats/generate_pool_statistics/test1.tsv", "src/stats/generate_pool_statistics/test2.tsv"],
+    "nrReadsNrGenesPerChromPool": "nrReadsNrGenesPerChrom_pool.txt"
+}
+
+### VIASH END
+
+INDEX_COL = ["WellBC", "WellID"]
+
+if __name__ == "__main__":
+    #########
+    # nrReadsNrGenesPerChrom file
+    #########
+    nr_reads_nr_genes_wells = []
+    par["nrReadsNrGenesPerChrom"] = list(map(Path, par["nrReadsNrGenesPerChrom"]))
+    for nr_reads_nr_genes_file in par["nrReadsNrGenesPerChrom"]:
+        nr_reads_nr_gene_well = pd.read_csv(nr_reads_nr_genes_file,
+                                            header=0, delimiter="\t",
+                                            dtype={"WellBC": pd.StringDtype(),
+                                                   "WellID": pd.StringDtype(),
+                                                   "Chr": pd.StringDtype(),
+                                                   "NumberOfReads": pd.UInt64Dtype(),
+                                                   "NumberOfGenes": pd.UInt64Dtype()})
+        if nr_reads_nr_gene_well.empty:
+            raise ValueError(f"{nr_reads_nr_genes_file.name} does not seem to contain any information!")
+        nr_reads_nr_genes_wells.append(nr_reads_nr_gene_well)
+    nr_reads_nr_genes_pool = pd.concat(nr_reads_nr_genes_wells, ignore_index=True,)
+    total_nr_reads_per_chromosome = nr_reads_nr_genes_pool.pivot_table(index=INDEX_COL, columns="Chr",
+                                                                       values=["NumberOfReads"], fill_value=0,
+                                                                       aggfunc="sum").droplevel(0, axis=1)
+    total_nr_reads_per_chromosome.columns.name = None
+    # Remove scaffolds/chromosomes with no counts
+    total_nr_reads_per_chromosome = total_nr_reads_per_chromosome.loc[:, (total_nr_reads_per_chromosome != 0).any(axis=0)]
+    ##### Total number of genes from all chromosomes
+    total_nr_genes = nr_reads_nr_genes_pool.loc[:, INDEX_COL + ['NumberOfGenes']].groupby(["WellBC", "WellID"]).sum()
+
+    ##### Total counts across (irrespective of chromosome)
+    total_sum_of_reads = total_nr_reads_per_chromosome.sum(numeric_only=True, axis=1) 
+
+    ##### Logic to split up chromosome per type
+    chromosome_names = total_nr_reads_per_chromosome.columns.to_list()
+    chr_regex = re.compile(r"^(chr)?\d+")
+    matching_chromosomes = [chr_name for chr_name 
+                            in chromosome_names
+                            if chr_regex.match(chr_name)]
+    sex_chromosome_names = ["X", "Y"]
+    mitochondrial_chr_name = "MT"
+    # This is logic from the original HT pipeline,
+    # only when all of the matched chromosomes start with "chr", the mitochonrial, X and Y
+    # chromosomes should also start with 'chr'
+    if all(chr_name.startswith("chr") for chr_name in matching_chromosomes):
+       sex_chromosome_names += ["chrX", "chrY"]
+       mitochondrial_chr_name = "chrM"
+
+    ###### Counts for mitochondrial reads
+    try:
+        mitochondrial_reads = total_nr_reads_per_chromosome.loc[:,mitochondrial_chr_name]
+    except KeyError:
+       mitochondrial_reads = 0
+    percentage_mitochondrial_reads = round(mitochondrial_reads / total_sum_of_reads * 100, 2)
+
+    ###### Counts for ERCC reads
+    total_ercc_reads = total_nr_reads_per_chromosome.filter(regex=r"^ERCC").sum(axis=1)
+    percentage_ercc_reads = round(total_ercc_reads / total_sum_of_reads * 100, 2)
+
+    ###### Counts for nuclear chromosomes
+    total_chromosomal_reads = total_nr_reads_per_chromosome.loc[:,matching_chromosomes].sum(axis=1)
+    percentage_chromosomal_reads = round(total_chromosomal_reads / total_sum_of_reads * 100, 2)
+
+    cols_to_add = {
+        "pctChrom": percentage_chromosomal_reads,
+        "pctMT": percentage_mitochondrial_reads,
+        "pctERCC": percentage_ercc_reads,
+        "SumReads": total_sum_of_reads,
+        "NumberOfGenes": total_nr_genes,
+        "NumberOfERCCReads": total_ercc_reads,
+        "NumberOfChromReads": total_chromosomal_reads,
+        "NumberOfMTReads": mitochondrial_reads,
+    }
+    total_nr_reads_per_chromosome = total_nr_reads_per_chromosome.assign(
+       **cols_to_add
+    )
+
+    total_nr_reads_per_chromosome.reset_index(names=INDEX_COL)\
+        .to_csv(par["nrReadsNrGenesPerChromPool"], sep="\t",
+                header=True, index=False, float_format="%g",
+                columns=tuple(INDEX_COL) + tuple(chromosome_names) + tuple(cols_to_add.keys())
+               )
+
--- a/src/stats/generate_pool_statistics/test.py
+++ b/src/stats/generate_pool_statistics/test.py
@@ -0,0 +1,269 @@
+from uuid import uuid4
+from textwrap import dedent
+from subprocess import CalledProcessError
+import pandas as pd
+import re
+import pytest
+import sys
+from pathlib import Path
+
+### VIASH START
+meta = {
+    "resources_dir": "./src/stats/generate_pool_statistics/",
+    "executable": "target/executable/stats/generate_pool_statistics/generate_pool_statistics",
+    "config": "src/stats/generate_pool_statistics/config.vsh.yaml"
+}
+### VIASH END
+
+@pytest.fixture
+def random_path(tmp_path):
+    def wrapper(extension=None):
+        extension = "" if not extension else f".{extension}"
+        return Path(tmp_path / f"{uuid4()}{extension}")
+    return wrapper
+
+
+@pytest.fixture
+def random_tsv_path(random_path):
+    def wrapper():
+        return random_path(".tsv")
+    return wrapper
+
+
+@pytest.fixture
+def simple_input_file_one(random_tsv_path, request):
+    prefix = request.param
+    mito_name = f"{prefix}M{'T' if not prefix else ''}"
+
+    contents = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    AGG	A1	{prefix}1	2	1
+    AGG	A1	{prefix}2	3	2
+    AGG	A1	{prefix}3	4	2
+    AGG	A1	{mito_name}	4	2
+    AGG	A1	{prefix}X	2	3
+    AGG	A1	ERCC-1	1	1
+    AGG	A1	ERCC-2	1	1
+    """)
+    output_file = random_tsv_path()
+    with output_file.open("w") as open_file:
+        open_file.write(contents)
+    return output_file
+
+
+@pytest.fixture
+def simple_input_file_two(random_tsv_path, request):
+    prefix = request.param
+    contents = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    CCC	B2	{prefix}2	2	1
+    CCC	B2	{prefix}3	3	2
+    CCC	B2	{prefix}5	4	2
+    CCC	B2	{prefix}1	4	2
+    CCC	B2	{prefix}Y	2	3
+    CCC	B2	{prefix}X	2	3
+    CCC	B2	ERCC-3	1	1
+    CCC	B2	ERCC-2	1	1
+    """)
+    output_file = random_tsv_path()
+    with output_file.open("w") as open_file:
+        open_file.write(contents)
+    return output_file
+
+@pytest.fixture
+def empty_input_file(random_tsv_path):
+    contents = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    """)
+    output_file = random_tsv_path()
+    with output_file.open("w") as open_file:
+        open_file.write(contents)
+    return output_file
+
+
+@pytest.mark.parametrize("simple_input_file_one,simple_input_file_two,expected", [("chr", "chr", "chr"), ("", "", "")], 
+                         indirect=["simple_input_file_one", "simple_input_file_two"])
+def test_generate_pool_statistics_simple(run_component, simple_input_file_one,
+                                         simple_input_file_two, random_tsv_path, expected):
+    
+    output_path = random_tsv_path()
+    run_component([
+        "--nrReadsNrGenesPerChrom", simple_input_file_one,
+        "--nrReadsNrGenesPerChrom", simple_input_file_two,
+        "--nrReadsNrGenesPerChromPool", output_path
+    ])
+    mito_name = f"{expected}M{'T' if not expected else ''}"
+    expected_dict = {
+        "WellBC": ["AGG", "CCC"],
+        "WellID": ["A1", "B2"],
+        "ERCC-1": ["1", "0"],
+        "ERCC-2": ["1", "1"],
+        "ERCC-3": ["0", "1"],
+        f"{expected}1": ["2", "4"],
+        f"{expected}2": ["3", "2"],
+        f"{expected}3": ["4", "3"],
+        f"{expected}5": ["0", "4"],
+        f"{mito_name}": ["4", "0"],
+        f"{expected}X": ["2", "2"],
+        f"{expected}Y": ["0", "2"],
+        "SumReads": ["17", "19"],
+        "pctMT": ["23.53", "0"],
+        "pctERCC": ["11.76", "10.53"],
+        "pctChrom": ["52.94", "68.42"],
+        "NumberOfGenes": ["12", "15"],
+        "NumberOfMTReads": ["4", "0"],
+        "NumberOfChromReads": ["9", "13"],
+        "NumberOfERCCReads": ["2", "2"],
+    }
+    expected_frame = pd.DataFrame.from_dict(expected_dict, dtype=pd.StringDtype())
+    assert output_path.is_file()
+    contents = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(contents, expected_frame, check_like=True)
+
+
+def test_only_numerical_chromosomes(run_component, random_tsv_path):
+    """
+    The chromosome column might be read as an integer instead of a string,
+    make sure that a numerical column only works.
+    """
+    output_path = random_tsv_path()
+    contents1 = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    CCC	B2	2	2	1
+    CCC	B2	3	3	2
+    CCC	B2	5	4	2
+    CCC	B2	1	4	2
+    """)
+    input_file_1 = random_tsv_path()
+    with input_file_1.open("w") as open_file:
+        open_file.write(contents1)
+
+    contents2 = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    AGG	A1	2	2	1
+    AGG	A1	3	3	2
+    AGG	A1	5	4	2
+    AGG	A1	1	4	2
+    """)
+    input_file_2 = random_tsv_path()
+    with input_file_2.open("w") as open_file:
+        open_file.write(contents2)
+        output_path = random_tsv_path()
+    run_component([
+        "--nrReadsNrGenesPerChrom", input_file_1,
+        "--nrReadsNrGenesPerChrom", input_file_2,
+        "--nrReadsNrGenesPerChromPool", output_path
+    ])
+
+    expected_dict = {
+        "WellBC": ["AGG", "CCC"],
+        "WellID": ["A1", "B2"],
+        "1": ["4", "4"],
+        "2": ["2", "2"],
+        "3": ["3", "3"],
+        "5": ["4", "4"],
+        "pctChrom": ["100", "100"],
+        "pctMT": ["0", "0"],
+        "pctERCC": ["0", "0"],
+        "SumReads": ["13", "13"],
+        "NumberOfGenes": ["7", "7"],
+        "NumberOfERCCReads": ["0", "0"],
+        "NumberOfChromReads": ["13", "13"],
+        "NumberOfMTReads": ["0", "0"],
+    }
+    expected_frame = pd.DataFrame.from_dict(expected_dict,
+                                            dtype=pd.StringDtype())
+
+    assert output_path.is_file()
+    contents = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(contents, expected_frame, check_like=True)
+
+
+@pytest.mark.parametrize("simple_input_file_one", [("")],
+                         indirect=["simple_input_file_one"])
+def test_empty_input_raises(run_component, simple_input_file_one, empty_input_file, random_tsv_path):
+    """
+    When an input file contains no data, raise an error.
+    """
+    output_path = random_tsv_path()
+    with pytest.raises(CalledProcessError) as err:
+        run_component([
+            "--nrReadsNrGenesPerChrom", simple_input_file_one,
+            "--nrReadsNrGenesPerChrom", empty_input_file,
+            "--nrReadsNrGenesPerChromPool", output_path
+        ])
+    assert re.search(
+        rf"{empty_input_file.name} does not seem to contain any information",
+        err.value.stdout.decode("utf-8"),
+    )
+
+def test_remove_chromosomes_with_no_counts(run_component, random_tsv_path):
+    """
+    If a chromosome has no counts across all of the wells, it should
+    not be included in the output
+    """
+    output_path = random_tsv_path()
+    contents1 = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    CCC	B2	2	2	1
+    CCC	B2	3	3	2
+    CCC	B2	5	4	2
+    CCC	B2	1	4	2
+    CCC	B2	empty	0	0
+    """)
+    input_file_1 = random_tsv_path()
+    with input_file_1.open("w") as open_file:
+        open_file.write(contents1)
+
+    contents2 = dedent(
+    f"""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    AGG	A1	2	2	1
+    AGG	A1	3	3	2
+    AGG	A1	5	4	2
+    AGG	A1	1	4	2
+    AGG	A1	empty	0	0
+    """)
+    input_file_2 = random_tsv_path()
+    with input_file_2.open("w") as open_file:
+        open_file.write(contents2)
+        output_path = random_tsv_path()
+    run_component([
+        "--nrReadsNrGenesPerChrom", input_file_1,
+        "--nrReadsNrGenesPerChrom", input_file_2,
+        "--nrReadsNrGenesPerChromPool", output_path
+    ])
+    # Here, the chromosome called "empty" should not be included
+    expected_dict = {
+        "WellBC": ["AGG", "CCC"],
+        "WellID": ["A1", "B2"],
+        "1": ["4", "4"],
+        "2": ["2", "2"],
+        "3": ["3", "3"],
+        "5": ["4", "4"],
+        "pctChrom": ["100", "100"],
+        "pctMT": ["0", "0"],
+        "pctERCC": ["0", "0"],
+        "SumReads": ["13", "13"],
+        "NumberOfGenes": ["7", "7"],
+        "NumberOfERCCReads": ["0", "0"],
+        "NumberOfChromReads": ["13", "13"],
+        "NumberOfMTReads": ["0", "0"],
+    }
+    expected_frame = pd.DataFrame.from_dict(expected_dict,
+                                            dtype=pd.StringDtype())
+
+    assert output_path.is_file()
+    contents = pd.read_csv(output_path, sep="\t", dtype=pd.StringDtype())
+    pd.testing.assert_frame_equal(contents, expected_frame, check_like=True)
+
+
+
+if __name__ == '__main__':
+    sys.exit(pytest.main([__file__]))
--- a/src/stats/generate_well_statistics/config.vsh.yaml
+++ b/src/stats/generate_well_statistics/config.vsh.yaml
@@ -0,0 +1,93 @@
+name: generate_well_statistics
+namespace: "stats"
+description: Generate summary statistics from BAM files generated by STAR solo.
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ author, maintainer ]
+  - __merge__: /src/base/authors/marijke_van_moerbeke.yaml
+    roles: [ contributor ]
+argument_groups:
+  - name: "Arguments"
+    arguments:
+      - name: "--input"
+        type: file
+        description: "The .bam file as returned by the mapping tool STAR."
+        direction: input
+        example: "input.bam"
+      - name: "--barcode"
+        type: string
+        description: |
+          The barcode for the well that is being processed. Is only used to add a metadata
+          column to all output files.
+        required: true
+      - name: "--well_id"
+        type: string
+        description: |
+          ID of this well. Only used to add a metadata column to the output files.
+        required: true
+      - name: "--processedBAMFile"
+        type: file
+        description: |
+          Path to a .tsv file listing, per read in the BAM file,
+          the value for the "CB", "UX", "GX" and "GN" tag, together with the
+          chromsome to which the read was mapped to.
+        direction: output
+        default: processedBamFile.txt
+      - name: "--nrReadsNrGenesPerChrom"
+        type: file
+        description: |
+          Path to an output file that contains a .tsv formatted table describing
+          per chromosome the number of reads that were mapped to that chromosome (NumberOfReads
+          column) and the number of genes on that chromosome that had at least one
+          read mapped to it (NumberOfGenes).
+        default: nrReadsNrGenesPerChrom.txt
+        direction: output
+      - name: "--nrReadsNrUMIsPerCB"
+        type: file
+        description: |
+          Path to an output file that contains a .tsv formatted table describing
+          per barcode the number of UMI's (nrUMIs) and the total number of reads (NumberOfReads).
+        direction: output
+        default: nrReadsNrUMIsPerCB.txt
+      - name: "--umiFreqTop"
+        type: file
+        description: |
+          Path to an output file that contains a .tsv formatted table describing
+          per UMI (column UB) the frequency at which they occur in the reads (column
+          N). Only the top 100 UMIs are included.
+        default: umiFreqTop100.txt
+        direction: output
+      - name: "--threads"
+        type: integer
+        description: |
+          Number of threads to use for decompressing BAM files.
+        min: 1
+        default: 1
+resources:
+- type: python_script
+  path: script.py
+
+test_resources:
+  - type: python_script
+    path: test.py
+  - path: test.sam
+  - path: empty.sam
+
+engines:
+  - type: docker
+    image: python:3.13-trixie
+    setup:
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - pysam
+          - pandas
+    test_setup:
+      - type: python
+        packages:
+          - viashpy
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/stats/generate_well_statistics/empty.sam
+++ b/src/stats/generate_well_statistics/empty.sam
@@ -0,0 +1,3 @@
+@HD	VN:1.4	SO:coordinate
+@SQ	SN:1	LN:200
+@SQ	SN:2	LN:50
--- a/src/stats/generate_well_statistics/script.py
+++ b/src/stats/generate_well_statistics/script.py
@@ -0,0 +1,83 @@
+import pysam
+import pandas as pd
+import logging
+
+### VIASH START
+par = {
+    "input": "src/stats/generate_well_statistics/test.sam",
+    "processedBAMFile": "processedBamFile.txt",
+    "nrReadsNrGenesPerChrom": "nrReadsNrGenesPerChrom.txt",
+    "nrReadsNrUMIsPerCB": "nrReadsNrUMIsPerCB.txt",
+    "umiFreqTop": "umiFreqTop.txt",
+    "threads": 1,
+    "barcode": "ACGT",
+    "well_id": "A1",
+}
+### VIASH END
+logger = logging.getLogger()
+console_handler = logging.StreamHandler()
+logger.addHandler(console_handler)
+logger.setLevel(logging.DEBUG)
+
+if __name__ == "__main__":
+    logger.info("Component started.")
+    parameters_str = [f'\t{param}: {param_val}\n' for param, param_val in par.items()]
+    logger.info("Parameters:\n%s", "".join(parameters_str).rstrip())
+    logger.info("Opening '%s'", par["input"])
+    samfile = pysam.AlignmentFile(par["input"], "rb", threads=par["threads"])
+    all_tags = []
+    index = []
+    tags_selection = ("CB", "UB", "GX", "GN")
+    for aligned_segment in samfile:
+        tags = dict(aligned_segment.get_tags())
+        all_tags.append(tags)
+        reference_name = aligned_segment.reference_name
+        index.append("*" if not reference_name else reference_name)
+    if not index:
+        # Workaround for https://github.com/pandas-dev/pandas/issues/58594
+        tag_dataframe = pd.DataFrame([], index=[], columns=tags_selection)
+    else:
+        tag_dataframe = pd.DataFrame.from_records(all_tags, index=index,
+                                                columns=tags_selection)
+    tag_dataframe_to_write = tag_dataframe.copy()
+    logger.info("Done reading BAM file. Found %i entries", tag_dataframe.shape[0])
+    tag_dataframe.assign(WellBC=par["barcode"], WellID=par["well_id"])\
+        .reset_index(names="Chr")\
+        .to_csv(par["processedBAMFile"], sep="\t", na_rep="",
+                header=True, index=False,
+                columns=("WellBC", "WellID", "Chr") + tags_selection)
+    logger.info("Constructing of dataframe done.")
+    # Number of genes that had a read mapped to them per chromosome,
+    # and the number of reads mapped to those genes per chromosome.
+    nr_reads_nr_genes = tag_dataframe.dropna(subset=["GX"]).groupby(level=0).agg(
+        NumberOfReads=pd.NamedAgg("GX", aggfunc="size"),
+        NumberOfGenes=pd.NamedAgg(column="GX", aggfunc="nunique")
+    )
+    nr_reads_nr_genes = nr_reads_nr_genes.reindex(samfile.header.references, fill_value=0)
+    logger.info("Done calculating number of reads per gene and per chromesome. Writing to %s",
+                par['nrReadsNrGenesPerChrom'])
+    nr_reads_nr_genes.reset_index(names="Chr").assign(WellBC=par["barcode"], WellID=par["well_id"])\
+        .to_csv(par["nrReadsNrGenesPerChrom"], sep="\t",
+                header=True, index=False, 
+                columns=("WellBC", "WellID", "Chr", "NumberOfReads", "NumberOfGenes"))
+
+    # Number of reads mapped to the reference, grouped by UMI
+    nr_read_per_umi = tag_dataframe.groupby('UB').size()\
+        .drop("", errors="ignore").sort_values(ascending=False).head(100)
+    nr_read_per_umi_df = nr_read_per_umi.to_frame(name="N")
+    logger.info("Done calculating number of mapped reads per UMI, writing to %s", par["umiFreqTop"])
+    nr_read_per_umi_df.assign(WellBC=par["barcode"], WellID=par["well_id"]).reset_index(names="UB")\
+        .to_csv(par["umiFreqTop"], header=True, sep="\t", 
+                index=False, columns=("WellBC", "WellID", "UB", "N"))
+
+    # Total number of mapped reads and total number of UMIs (not grouped per chromosome)
+    nr_reads_and_umi_per_barcode = tag_dataframe.groupby(by="CB").agg(
+        NumberOfReads=pd.NamedAgg("CB", "size"),
+        nrUMIs=pd.NamedAgg("UB", "nunique")
+    )
+    logger.info("Done calculating number of mapped reads and number of UMIs per Cell Barcode, writing to %s",
+                par["nrReadsNrUMIsPerCB"])
+    nr_reads_and_umi_per_barcode.assign(WellBC=par["barcode"], WellID=par["well_id"]).reset_index(names="CB")\
+        .to_csv(par["nrReadsNrUMIsPerCB"], sep="\t", header=True, 
+                index=False, columns=("WellBC", "WellID", "CB", "NumberOfReads", "nrUMIs"))
+    logger.info("Finished!")
--- a/src/stats/generate_well_statistics/test.py
+++ b/src/stats/generate_well_statistics/test.py
@@ -0,0 +1,166 @@
+import sys
+import pytest
+import pysam
+from uuid import uuid4
+from pathlib import Path
+from textwrap import dedent
+
+### VIASH START
+meta = {
+    "resources_dir": "./src/stats/generate_well_statistics/",
+    "executable": "target/executable/stats/generate_well_statistics/generate_well_statistics",
+    "config": "src/stats/generate_well_statistics/config.vsh.yaml"
+}
+### VIASH END
+
+def assert_file_content_equals(file_to_check, expected):
+    with file_to_check.open('r') as open_file:
+        contents = open_file.read()
+        assert contents == expected
+
+
+@pytest.fixture
+def input_sam_path():
+    return Path(meta["resources_dir"]) / "test.sam"
+
+
+@pytest.fixture
+def random_path(tmp_path):
+    def wrapper(extension=None):
+        extension = "" if not extension else f".{extension}"
+        return tmp_path / f"{uuid4()}{extension}"
+    return wrapper 
+
+@pytest.fixture
+def random_bam_path(random_path):
+    def wrapper():
+        return random_path(".bam")
+    return wrapper
+
+
+@pytest.fixture
+def sam_to_bam(random_bam_path):
+    def wrapper(sam_file):
+        out_path = random_bam_path()
+        with pysam.AlignmentFile(sam_file, "r") as infile, \
+            pysam.AlignmentFile(out_path, "wb", template=infile) as outfile:
+            for s in infile:
+                outfile.write(s)
+        infile.close()
+        return out_path
+    return wrapper
+
+
+@pytest.fixture
+def empty_sam_path():
+    return Path(meta["resources_dir"]) / "empty.sam"
+
+
+def test_generate_well_statistics_simple_bam(run_component, input_sam_path, sam_to_bam, random_path):
+    bam_file = sam_to_bam(input_sam_path)
+    processed_bam = random_path("tsv")
+    reads_per_chromosome = random_path("tsv")
+    nr_reads_nr_umis_per_cb = random_path("tsv")
+    top_onehundred_umis = random_path("tsv")
+    run_component([
+        "--input", bam_file,
+        "--processedBAMFile", processed_bam,
+        "--nrReadsNrGenesPerChrom", reads_per_chromosome,
+        "--nrReadsNrUMIsPerCB", nr_reads_nr_umis_per_cb,
+        "--umiFreqTop", top_onehundred_umis,
+        "--barcode", "ACGT",
+        "--well_id", "A1",
+    ])
+    for file_path in (processed_bam, reads_per_chromosome,
+                      nr_reads_nr_umis_per_cb, top_onehundred_umis):
+        assert file_path.is_file()
+
+    expected_processed_bam = \
+    dedent("""\
+    WellBC	WellID	Chr	CB	UB	GX	GN
+    ACGT	A1	1	ACA	CGG	gene1	gene1
+    ACGT	A1	1	ACA	CGG	gene1	gene1
+    ACGT	A1	2	GGG	GTT	gene2	gene2
+    ACGT	A1	2	GGG	GTC	gene3	gene3
+    """)
+
+    expected_reads_per_chromosome = \
+    dedent("""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    ACGT	A1	1	2	1
+    ACGT	A1	2	2	2
+    """)
+
+    expected_nr_reads_nr_umis_per_cb = \
+    dedent("""\
+    WellBC	WellID	CB	NumberOfReads	nrUMIs
+    ACGT	A1	ACA	2	1
+    ACGT	A1	GGG	2	2
+    """)
+
+    expected_top_onehundred_umis = \
+    dedent("""\
+    WellBC	WellID	UB	N
+    ACGT	A1	CGG	2
+    ACGT	A1	GTC	1
+    ACGT	A1	GTT	1
+    """)
+
+    assert_file_content_equals(processed_bam, expected_processed_bam)
+    assert_file_content_equals(reads_per_chromosome, expected_reads_per_chromosome)
+    assert_file_content_equals(nr_reads_nr_umis_per_cb, expected_nr_reads_nr_umis_per_cb)
+    assert_file_content_equals(top_onehundred_umis, expected_top_onehundred_umis)
+
+
+def test_empty_sam(run_component, empty_sam_path, sam_to_bam, random_path):
+    """
+    Test an empty bam file. Make sure that chromosomes with mapped reads
+    are still represented. Ran into issue https://github.com/pandas-dev/pandas/pull/59258
+    """
+    bam_file = sam_to_bam(empty_sam_path)
+    processed_bam = random_path("tsv")
+    reads_per_chromosome = random_path("tsv")
+    nr_reads_nr_umis_per_cb = random_path("tsv")
+    top_onehundred_umis = random_path("tsv")
+    run_component([
+        "--input", bam_file,
+        "--processedBAMFile", processed_bam,
+        "--nrReadsNrGenesPerChrom", reads_per_chromosome,
+        "--nrReadsNrUMIsPerCB", nr_reads_nr_umis_per_cb,
+        "--umiFreqTop", top_onehundred_umis,
+        "--barcode", "ACGT",
+        "--well_id", "A1",
+    ])
+    for file_path in (processed_bam, reads_per_chromosome,
+                      nr_reads_nr_umis_per_cb, top_onehundred_umis):
+        assert file_path.is_file()
+
+    expected_processed_bam = \
+    dedent("""\
+    WellBC	WellID	Chr	CB	UB	GX	GN
+    """)
+
+    expected_reads_per_chromosome = \
+    dedent("""\
+    WellBC	WellID	Chr	NumberOfReads	NumberOfGenes
+    ACGT	A1	1	0	0
+    ACGT	A1	2	0	0
+    """)
+
+    expected_nr_reads_nr_umis_per_cb = \
+    dedent("""\
+    WellBC	WellID	CB	NumberOfReads	nrUMIs
+    """)
+
+    expected_top_onehundred_umis = \
+    dedent("""\
+    WellBC	WellID	UB	N
+    """)
+
+    assert_file_content_equals(processed_bam, expected_processed_bam)
+    assert_file_content_equals(reads_per_chromosome, expected_reads_per_chromosome)
+    assert_file_content_equals(nr_reads_nr_umis_per_cb, expected_nr_reads_nr_umis_per_cb)
+    assert_file_content_equals(top_onehundred_umis, expected_top_onehundred_umis)
+
+if __name__ == '__main__':
+    sys.exit(pytest.main([__file__]))
--- a/src/stats/generate_well_statistics/test.sam
+++ b/src/stats/generate_well_statistics/test.sam
@@ -0,0 +1,7 @@
+@HD	VN:1.4	SO:coordinate
+@SQ	SN:1	LN:200
+@SQ	SN:2	LN:50
+test_1	16	1	22	255	1M	*	0	0	C	I	NH:i:1	HI:i:1	nM:i:0	AS:i:47	CR:Z:ACA	UR:Z:CGG	GX:Z:gene1	GN:Z:gene1	CB:Z:ACA	UB:Z:CGG
+test_2	16	1	22	255	1M	*	0	0	G	!	NH:i:1	HI:i:1	nM:i:0	AS:i:47	CR:Z:ACA	UR:Z:CGG	GX:Z:gene1	GN:Z:gene1	CB:Z:ACA	UB:Z:CGG
+test_3	0	2	40	255	1M	*	0	0	T	!	NH:i:1	HI:i:1	nM:i:0	AS:i:47	CR:Z:GGG	UR:Z:GTT	GX:Z:gene2	GN:Z:gene2	CB:Z:GGG	UB:Z:GTT
+test_4	0	2	60	255	1M	*	0	0	C	!	NH:i:1	HI:i:1	nM:i:0	AS:i:47	CR:Z:GGG	UR:Z:GTC	GX:Z:gene3	GN:Z:gene3	CB:Z:GGG	UB:Z:GTC
--- a/src/utils/concatRuns/config.vsh.yaml
+++ b/src/utils/concatRuns/config.vsh.yaml
@@ -0,0 +1,43 @@
+name: concatRuns
+namespace: utils
+description: |
+  Concatenate well FASTQ files from different runs in order to increase sequencing depth.
+arguments:
+  - name: "--input_r1"
+    type: file
+    required: true
+    multiple: true
+  - name: "--input_r2"
+    type: file
+    required: true
+    multiple: true
+  - name: "--sample_id"
+    type: string
+    required: true
+  - name: "--output_r1"
+    type: file
+    multiple: true
+    description: Path to read 1 fastq/fasta file
+    direction: output
+  - name: "--output_r2"
+    type: file
+    multiple: true
+    description: Path to read 2 fastq/fasta file
+    direction: output
+resources:
+  - type: nextflow_script
+    path: main.nf
+    entrypoint: run_wf
+dependencies:
+    - name: concat_text
+      repository: cb
+repositories:
+  - name: cb
+    type: vsh
+    repo: craftbox
+    tag: v0.3.0
+runners:
+  - type: nextflow
+
+engines:
+  - type: native
--- a/src/utils/concatRuns/main.nf
+++ b/src/utils/concatRuns/main.nf
@@ -0,0 +1,128 @@
+workflow run_wf {
+
+  take:
+    input_ch
+
+  main:
+    // Count the number of input events per sample
+    // Results from events with the same sample ID need to be concatenated.
+    event_counts_ch = input_ch
+      | map {id, state ->
+        def new_state = state + ["event_id": id]
+        def new_event = [state.sample_id, new_state]
+        return new_event
+      }
+      | groupTuple(by: 0)
+      | flatMap { id, states ->
+          def orig_event_ids = states.collect{it.event_id}
+          def new_events = orig_event_ids.collect{ orig_event_id ->
+            [orig_event_id, ["n_events": states.size()]]
+           }
+          return new_events
+      }
+
+
+    // The number of events per sample needs is passed number to `groupTuple()`
+    // so that it can emit the sample as soon as it is ready. This makes sure
+    // that the samples are processed asynchronously. 
+    output_ch = input_ch.join(event_counts_ch)
+      | flatMap {id, state_demultiplex, state_event_counts ->
+          assert state_demultiplex.input_r1.size() == state_demultiplex.input_r2.size(),
+            "Expected output from well demultiplexing to contain equal amount or forward and reverse FASTQ files."
+          def new_states = [state_demultiplex.input_r1, state_demultiplex.input_r2].transpose().collect{ fastq_files ->
+            def (r1_file, r2_file) = fastq_files
+            def regex = ~/^(\w+)_R[12]{1}_001\.fastq(\.gz)?$/
+            def parsed_file_name = r1_file.name =~ regex
+            def parsed_file_name_r2 = r2_file.name =~ regex
+            def well_id = parsed_file_name[0][1]
+            def well_id_r2 = parsed_file_name_r2[0][1]
+  
+            assert (well_id.length() != 0) && (well_id == well_id_r2)
+            def new_state = state_demultiplex + [
+              "input_r1": r1_file,
+              "input_r2": r2_file,
+              "event_id": id,
+            ]
+            def group_settings = groupKey("${state_demultiplex.sample_id}_${well_id}", state_event_counts.n_events)
+            return [group_settings, new_state]
+
+          }
+        return new_states 
+      }
+      | groupTuple(by: 0, sort: "hash", remainder: true)
+      | map {group_settings, sample_states -> 
+        def input_r1 = sample_states.collect{it.input_r1}.flatten()
+        def input_r2 = sample_states.collect{it.input_r2}.flatten()
+        def event_ids = sample_states.collect{it.event_id}
+        def sample_id_list = sample_states.collect{it.sample_id}.unique()
+        assert sample_id_list.size() == 1
+        def sample_id = sample_id_list[0]
+        assert input_r1.size() == input_r2.size()  
+        
+        def new_state = [
+          "input_r1": input_r1, 
+          "input_r2": input_r2,
+          "event_id": event_ids,
+          "sample_id": sample_id,
+        ]
+        return [group_settings.target, new_state]
+      } 
+      | concat_text.run(
+        directives: [label: ["lowmem", "lowcpu"]],
+        key: "concat_samples_r1",
+        runIf: {id, state -> state.input_r1.size() > 1},
+        fromState: { id, state ->
+          def output_file_name = state.input_r1[0].name
+          [
+            input: state.input_r1,
+            gzip_output: false,
+            output: output_file_name
+          ]
+        },
+        toState: { id, result, state ->
+          def newState = state + [ input_r1: [ result.output ] ]
+          return newState
+        }
+      )
+      | concat_text.run(
+        directives: [label: ["lowmem", "lowcpu"]],
+        key: "concat_samples_r2",
+        runIf: {id, state -> state.input_r2.size() > 1},
+        fromState: { id, state ->
+          def output_file_name = state.input_r2[0].name
+          [
+            input: state.input_r2,
+            gzip_output: false,
+            output: output_file_name
+          ]
+        },
+        toState: { id, result, state ->
+          def newState = state + [ input_r2: [ result.output ] ]
+          return newState
+        }
+      )
+      | map {id, state ->
+          def new_state = [state.sample_id, state]
+          return new_state
+      }
+      | groupTuple(by: 0, sort: 'hash')
+      | map {id, states ->
+        def new_state = [
+          "input_r1": states.collect{it.input_r1}.flatten(),
+          "input_r2": states.collect{it.input_r2}.flatten(),
+          "_meta": ["join_id": states[0].event_id[0]]
+        ]
+        return [id, new_state]
+      }
+      | setState(
+        [
+          "output_r1": "input_r1",
+          "output_r2": "input_r2",
+          "_meta": "_meta"
+        ]
+      )
+
+  emit: 
+    output_ch
+
+}
--- a/src/utils/listInputDir/config.vsh.yaml
+++ b/src/utils/listInputDir/config.vsh.yaml
@@ -0,0 +1,45 @@
+name: listInputDir
+namespace: utils
+description: List the contents of a directory and parse contained fastq files
+arguments:
+  - name: "--input"
+    alternatives: [-i]
+    type: file
+    description: Path to the directory containing fastq files
+    required: true
+    example: fastq_dir
+  - name: --pools
+    description: "Pool names to include. By default all pools are selected for analysis."
+    type: string
+    multiple: true
+  - name: "--r1_output"
+    type: file
+    description: Path to read 1 fastq/fasta file
+    direction: output
+  - name: "--r2_output"
+    type: file
+    description: Path to read 2 fastq/fasta file
+    direction: output
+  - name: "--lane"
+    type: string
+    description: Lane nr
+    direction: output
+  - name: "--sample"
+    type: string
+    description: Sample nr
+    direction: output
+  - name: "--sample_id"
+    type: string
+    description: Sample name
+    direction: output
+
+resources:
+  - type: nextflow_script
+    path: main.nf
+    entrypoint: run_wf
+
+runners:
+  - type: nextflow
+
+engines:
+  - type: native
--- a/src/utils/listInputDir/main.nf
+++ b/src/utils/listInputDir/main.nf
@@ -0,0 +1,72 @@
+workflow run_wf {
+
+  take: in_
+
+  main:
+
+    out_ = in_
+      | flatMap{ id, state ->
+        println "Looking for fastq files in ${state.input}"
+        def allFastqs = state.input
+          .listFiles()
+          .findAll{
+            it.isFile() &&
+            it.name ==~ /^.+\.fastq.gz$|^.+\.fastq$|^.+\.fasta$/
+          }
+        println "Found ${allFastqs.size()} fastq/fasta files in ${state.input}"
+        assert allFastqs.size() > 0: "No fastq/fasta files found"
+
+        println("Extracting information from fastq/fasta filenames")
+        def processed_fastqs = allFastqs.collect { f ->
+          def regex = ~/^(\S+)_S(\d+)_(L(\d+)_)?R(\d)_(\d+)\.fast[qa](\.gz)?$/
+          def validFastq = f.name ==~ regex
+
+          assert validFastq: "${f} does not match the regex ${regex}"
+
+          def parsedFastq = f.name =~ regex
+          def lane = parsedFastq[0][3]
+          // Remove the trailing '_'
+          def lane_remove_trailing = lane == null ? "" : lane.replaceAll('_$', "")
+          def sample_id = parsedFastq[0][1]
+          if (sample_id in ["Undetermined"] || (state.pools && !state.pools.isEmpty() && !state.pools.contains(sample_id))) {
+            return null
+          }
+          return [
+            "fastq": f,
+            "sample_id": sample_id,
+            "sample": parsedFastq[0][2],
+            "lane": lane_remove_trailing,
+            "read": parsedFastq[0][5],
+          ]
+        }
+
+        println("Group paired fastq/fasta files")
+        def grouped = processed_fastqs
+          .findAll{it != null}
+          .groupBy({it.sample_id}, {it.lane})
+          .collectMany{ sample_id, states_per_lane ->
+            def result = states_per_lane.collect{lane, lane_states ->
+              assert lane_states.size() == 2, "Expected to find two fastq files per lane! " +
+                "Found ${lane_states.size()}. State: ${states_per_lane}"
+              def r1_state = lane_states.find({it.read == "1"})
+              def r2_state = lane_states.find({it.read == "2"})
+              def fastq_state = [
+                "r1_output": r1_state.fastq,
+                "r2_output": r2_state.fastq
+              ]
+              def new_state = fastq_state +
+                r1_state.findAll{it.key in ["sample_id", "sample", "lane"]} + 
+                ["_meta": ["join_id": id]]
+              def new_id = lane?.trim() ? "${sample_id}_${lane}".toString() : sample_id
+              return [new_id, new_state]
+            }
+            return result
+
+          }
+          return grouped
+
+      }
+
+  emit: out_
+
+}
--- a/src/utils/save_params/config.vsh.yaml
+++ b/src/utils/save_params/config.vsh.yaml
@@ -0,0 +1,47 @@
+name: save_params
+namespace: utils
+description: |
+  Save parameters to a YAML file
+
+argument_groups:
+  - name: Inputs
+    arguments:
+      - name: "--id"
+        description: |
+          The id of the job
+        type: string
+        required: true
+      - name: "--params_yaml"
+        description: |
+          base64 encoded yaml containing the state
+        type: string
+        required: true
+    
+  - name: Outputs
+    arguments:
+      - name: "--output"
+        description: |
+          The output YAML file
+        type: file
+        direction: output
+        required: true
+        example: "output.yaml"
+
+resources:
+  - type: python_script
+    path: script.py
+
+engines:
+  - type: docker
+    image: python:3.12-slim
+    setup:
+      - type: apt
+        packages:
+          - procps
+      - type: python
+        packages:
+          - pyyaml
+
+runners:
+  - type: executable
+  - type: nextflow
--- a/src/utils/save_params/script.py
+++ b/src/utils/save_params/script.py
@@ -0,0 +1,28 @@
+import re
+import yaml
+import base64
+
+## VIASH START
+par = {
+    "id": "sample_one",
+    "params_yaml": "cGFyYW1zX3lhbWw6IHt9Cg==",
+    "output": "output.yaml"
+}
+## VIASH END
+
+class Dumper(yaml.Dumper):
+    def increase_indent(self, flow=False, indentless=False):
+        return super(Dumper, self).increase_indent(flow, False)
+
+def decode_params_yaml(encoded_yaml):
+    yaml_bytes = base64.b64decode(encoded_yaml)
+    yaml_string = yaml_bytes.decode('utf-8')
+    yaml_data = yaml.safe_load(yaml_string)
+    
+    return yaml_data
+
+params = decode_params_yaml(par['params_yaml'])
+
+with open(par["output"], 'w') as f:
+    yaml.dump(params, f, default_flow_style=False, Dumper=Dumper)
+
--- a/src/utils/save_params/test.py
+++ b/src/utils/save_params/test.py
--- a/src/workflows/htrnaseq/config.vsh.yaml
+++ b/src/workflows/htrnaseq/config.vsh.yaml
@@ -0,0 +1,144 @@
+name: htrnaseq
+namespace: workflows
+authors:
+  - __merge__: /src/base/authors/dries_schaumont.yaml
+    roles: [ maintainer ]
+argument_groups:
+  - name: Input arguments
+    arguments:
+      - name: --input_r1
+        description: |
+          Forward reads in FASTQ format. Multiple files corresponding to different lanes can be provided which will
+          be demultiplexed separately before joining the results for each individual well.
+        type: file
+        required: true
+        multiple: true
+      - name: --input_r2
+        description: |
+          Reverse reads in FASTQ format. Multiple files corresponding to different lanes can be provided which will
+          be demultiplexed separately before joining the results for each individual well.
+        type: file
+        required: true
+        multiple: true
+      - name: --barcodesFasta
+        type: file
+        required: true
+      - name: "--umi_length"
+        description: |
+          Length of the UMI sequences
+        type: integer
+        min: 1
+        default: 10
+      - name: --genomeDir
+        type: file
+        required: true
+      - name: --annotation
+        type: file
+        required: true
+      - name: --sample_id
+        type: string
+        required: false
+        description: |
+          Sample ID for the provided input files. If not provided, the value of --id
+          will be used. Input files will allways be demultiplexed separately,
+          but the FASTQs for wells with matching sample IDs will be concatenated before mapping.
+  - name: Output arguments
+    arguments:
+      - name: "--fastq_output"
+        description: "Directory containing output fastq files"
+        type: file
+        multiple: true
+        required: true
+        default: "fastq/*"
+        direction: output
+      - name: --star_output
+        description: Output from mapping with STAR
+        type: file
+        direction: output
+        multiple: true
+        required: true
+        default: star.$id/*
+      - name: "--nrReadsNrGenesPerChrom"
+        type: file
+        direction: output
+        required: true
+        default: "nrReadsNrGenesPerChrom.$id.txt"
+      - name: "--star_qc_metrics"
+        type: file
+        direction: output
+        required: true
+        default: "starLogs.$id.txt"
+      - name: "--eset"
+        type: file
+        direction: output
+        required: true
+        default: eset.$id.rds
+      - name: "--f_data"
+        type: file
+        direction: output
+        required: true
+        default: fData.$id.tsv
+      - name: "--p_data"
+        type: file
+        direction: output
+        required: true
+        default: pData.$id.tsv
+      - name: "--html_report"
+        type: file
+        direction: output
+        required: true
+        default: report.$id.html
+      - name: "--run_params"
+        type: file
+        direction: output
+        required: false
+        default: params.$id.yaml
+resources:
+  - type: nextflow_script
+    path: main.nf
+    entrypoint: run_wf
+test_resources:
+  - type: nextflow_script
+    path: test.nf
+    entrypoint: test_wf
+  - type: nextflow_script
+    path: test.nf
+    entrypoint: test_wf2
+dependencies:
+  - name: stats/combine_star_logs
+    repository: local
+  - name: stats/generate_pool_statistics
+    repository: local
+  - name: stats/generate_well_statistics
+    repository: local
+  - name: workflows/well_demultiplex
+    repository: local
+  - name: workflows/well_metadata
+    repository: local
+  - name: parallel_map
+    repository: local
+  - name: eset/create_eset
+    repository: local
+  - name: eset/create_fdata
+    repository: local
+  - name: eset/create_pdata
+    repository: local
+  - name: report/create_report
+    repository: local
+  - name: utils/concatRuns
+    repository: local
+  - name: utils/save_params
+    repository: local
+repositories:
+  - name: local
+    type: local
+  - name: bb
+    type: vsh
+    repo: biobox
+    tag: v0.3.1
+
+runners:
+  - type: nextflow
+
+engines:
+  - type: native
--- a/src/workflows/htrnaseq/integration_test.sh
+++ b/src/workflows/htrnaseq/integration_test.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+# get the root of the directory
+REPO_ROOT=$(git rev-parse --show-toplevel)
+
+# ensure that the command below is run from the root of the repository
+cd "$REPO_ROOT"
+
+# Make sure the workflow is built
+viash ns build --setup cb --parallel
+
+export NXF_VER=24.04.4
+
+set -eo pipefail
+
+nextflow \
+  run . \
+  -main-script src/workflows/htrnaseq/test.nf \
+  -config ./src/config/labels.config \
+  -entry test_wf \
+  -resume \
+  -profile docker,local \
+  --publish_dir output
+
+nextflow \
+  run . \
+  -main-script src/workflows/htrnaseq/test.nf \
+  -config ./src/config/labels.config \
+  -entry test_wf2 \
+  -resume \
+  -profile docker,local \
+  --publish_dir output2
+
--- a/src/workflows/htrnaseq/main.nf
+++ b/src/workflows/htrnaseq/main.nf
@@ -0,0 +1,359 @@
+workflow run_wf {
+  take:
+    raw_ch
+
+  main:
+    input_ch = raw_ch
+      // Use the event ID as the default for the sample ID
+      | map {id, state ->
+        def sample_id = state.sample_id ?: id 
+        def newState = state + ["sample_id": sample_id, "run_id": id]
+        return [id, newState]
+      }
+
+      | save_params.run(
+        runIf: { id, state ->
+          state.run_params != null
+        },
+        fromState: {id, state ->
+          // Define the function before using it
+          def convertPaths
+          convertPaths = { value ->
+            if (value instanceof java.nio.file.Path)
+              return value.toUriString()
+            else if (value instanceof List)
+              return value.collect { convertPaths(it) }
+            else if (value instanceof Collection)
+              throw new UnsupportedOperationException("Collections other than Lists are not supported")
+            else
+              return value
+          }
+          
+          // Apply conversion to all state values
+          def convertedState = state.collectEntries { k, v -> [(k): convertPaths(v)] }
+          
+          def yaml = new org.yaml.snakeyaml.Yaml()
+          def yamlString = yaml.dump(convertedState)
+          def encodedYaml = yamlString.bytes.encodeBase64().toString()
+          
+          return [
+            "id": id,
+            "params_yaml": encodedYaml,
+            "output": state.run_params
+          ]
+        },
+        toState: ["run_params": "output"]
+      )
+
+    // The featureData only has one requirement: the genome annotation.
+    // It can be generated straight away. Most of the time, there is one shared 
+    // annotation for all of the inputs and the fData should only be calculated once.
+    // The state is manpulated in such a way that there is one event created per unique
+    // input annotation file. In turn, the featureData file can joined into the original input
+    // channel which allows it to be shared across events if required.
+    f_data_ch = input_ch
+      | toSortedList()
+      | flatMap {ids_and_states ->
+        def annotation_files = ids_and_states.inject([:]){ old_state, id_and_state ->
+          def (id, state) = id_and_state
+          def annotation_file = state.annotation
+          def new_state = old_state + [(annotation_file): (old_state.getOrDefault(annotation_file, []) + [id])]
+          return new_state
+        }
+        def file_names = annotation_files.keySet().collect{it.name}
+        assert (file_names.toSet().size() == file_names.size()), 
+          "Please make sure that the annotation files have unique file names."
+        def new_states = annotation_files.collect{annotation_file, value ->
+          def new_state = [annotation_file.name , ["annotation": annotation_file, "event_ids": value]]
+          return new_state
+        }
+        return new_states 
+      }
+      | create_fdata.run(
+        directives: [label: ["lowmem", "lowcpu"]],
+        fromState: [
+          "gtf": "annotation",
+          "output": "f_data"
+        ],
+        toState: ["f_data": "output"]
+      )
+      | flatMap {_, state -> 
+          def new_states = state.event_ids.collect{event_id ->
+            [event_id, ["f_data": state.f_data]]
+          }
+          return new_states
+      }
+
+    // Perform mapping of each well.
+    demultiplex_ch = input_ch
+      | well_demultiplex.run(
+        fromState: [
+            "input_r1": "input_r1",
+            "input_r2": "input_r2",
+            "barcodesFasta": "barcodesFasta",
+        ],
+        toState: {id, result, state ->
+          def all_fastq = result.output_r1 + result.output_r2
+          def output_dir = all_fastq.collect{it.parent}.unique()
+          assert output_dir.size() == 1, "Expected output from well demultiplexing (id $id) to reside into one directory. Found: $output_dir"
+          def new_state = state + [
+            "input_r1": result.output_r1,
+            "input_r2": result.output_r2,
+            "fastq_output_directory": output_dir[0],
+          ]
+          return new_state
+        }
+      )
+
+    fastq_output_directory_ch = demultiplex_ch
+      | map {id, state ->
+        def new_event = [state.sample_id, state]
+        return new_event
+      }
+      | groupTuple(by: 0, sort: "hash")
+      | map {id, states ->
+        def fastq_output_dirs = states.collect{it.fastq_output_directory}
+        def new_state = ["fastq_output_directory": fastq_output_dirs]
+        def new_event = [id, new_state]
+        return [id, new_state]
+      }
+
+
+    concat_samples_ch = demultiplex_ch.join(f_data_ch)
+      | map {id, demultiplex_state, f_data_state ->
+        def newState = demultiplex_state + ["f_data": f_data_state["f_data"]]
+        [id, newState]
+      }
+      | concatRuns.run(
+        fromState: [
+          "input_r1": "input_r1",
+          "input_r2": "input_r2",
+          "sample_id": "sample_id",
+        ],
+        toState: {id, result, state ->
+          def state_overwite = [
+            "input_r1": result.output_r1,
+            "input_r2": result.output_r2,
+            "_meta": ["join_id": state.run_id]
+          ]
+          return state + state_overwite
+        }
+      )
+
+    pool_ch = concat_samples_ch.join(fastq_output_directory_ch)
+      | map {id, concat_state, fastq_output_directory_state ->
+        def new_state = concat_state + fastq_output_directory_state
+        return [id, new_state]
+      } 
+      | parallel_map.run(
+        directives: ["label": ["highmem", "lowcpu"]],
+        fromState: {id, state ->
+          [
+            "input_r1": state.input_r1,
+            "input_r2": state.input_r2,
+            "barcodesFasta": state.barcodesFasta,
+            "umiLength": state.umi_length,
+            "output": state.star_output[0],
+            "genomeDir": state.genomeDir,
+          ]
+        },
+        toState: [
+          "star_output": "output",
+        ]
+      )
+      // Split the events from 1 event per pool into events per well
+      // and add extra metadata about the wells to the state.
+      | well_metadata.run(
+        fromState: [
+          "barcodesFasta": "barcodesFasta",
+          "input_r1": "input_r1",
+          "input_r2": "input_r2",
+          "star_mapping": "star_output"
+        ],
+        toState: [
+          "input_r1": "output_r1",
+          "input_r2": "output_r2",
+          "pool": "pool",
+          "well_id": "well_id",
+          "barcode": "barcode",
+          "lane": "lane",
+          "n_wells": "n_wells",
+          "star_mapping": "well_star_mapping",
+        ]
+      )
+      // Use the bam file to generate statistics
+      | generate_well_statistics.run(
+        directives: [label: ["verylowmem", "verylowcpu"]],
+        fromState: { id, state ->
+          [
+            "input": state.star_mapping.resolve('Aligned.sortedByCoord.out.bam'),
+            "barcode": state.barcode,
+            "well_id": state.well_id,
+          ]
+        },
+        toState: [
+          "nrReadsNrGenesPerChromWell": "nrReadsNrGenesPerChrom",
+        ]
+      )
+      // Join the events back to pool-level
+      | map {id, state ->
+        // Create a special groupKey, such that groupTuple
+        // knows when all the barcodes have been grouped into 1 event.
+        // This way the processing is as distributed as possible.
+        def key = groupKey(state.pool, state.n_wells)
+        def newEvent = [key, state]
+        return newEvent
+      }
+      // Use a custom sorting function because sort: 'hash'
+      // requires a hash to be calculated on every entry of the state
+      // This is inefficient when the number of events is large 
+      // (i.e large number or barcodes).
+      // Sorting on lexographical order of the barcode is sufficient here.
+      | groupTuple(sort: {a, b -> a.barcode <=> b.barcode})
+      | map {id, states ->
+        // Gather the keys from all states. for some state items,
+        // we need gather all the different items from across the states
+        def barcodes = states.collect{it.barcode}
+        assert barcodes.clone().unique().size() == barcodes.size(), \
+          "Error when gathering information for pool ${id}, barcodes are not unique!"
+        def well_ids = states.collect{it.well_id}
+        assert well_ids.clone().unique().size() == well_ids.size(), \
+          "Error when gathering information for pool ${id}, well IDs are not unique!"
+        def custom_state = [
+          "input_r1": states.collect{it.input_r1},
+          "input_r2": states.collect{it.input_r2},
+          "barcode": barcodes,
+          "well_id": well_ids,
+          "star_mapping": states.collect{it.star_mapping},
+          // Well and pool stats should be carefully kept separate.
+          // The workflow argument points to the name for the pool statistics:
+          "nrReadsNrGenesPerChromWell": states.collect{it.nrReadsNrGenesPerChromWell},
+          "nrReadsNrGenesPerChromPool": states[0].nrReadsNrGenesPerChrom
+        ]
+        //For many state items, the value is the same across states.
+        def other_state_keys = states.inject([].toSet()){ current_keys, state ->
+            def new_keys = current_keys + state.keySet()
+            return new_keys
+          }.minus(custom_state.keySet())
+        // All other state should have a unique value
+        def old_state_items = other_state_keys.inject([:]){ old_state, argument_name ->
+            argument_values = states.collect{it.get(argument_name)}.unique()
+            assert argument_values.size() == 1, "Arguments should be the same across modalities. Please report this \
+                                                 as a bug. Argument name: $argument_name, \
+                                                 argument value: $argument_values"
+            def argument_value
+            argument_values.each { argument_value = it }
+            def current_state = old_state + [(argument_name): argument_value]
+            return current_state
+          }
+
+        def new_state = custom_state + old_state_items
+        [id.getGroupTarget(), new_state]
+      }
+
+    pool_statistics_ch = pool_ch
+      | generate_pool_statistics.run(
+        directives: ["label": ["lowmem", "verylowcpu"]],
+        fromState: [
+          "nrReadsNrGenesPerChrom": "nrReadsNrGenesPerChromWell",
+          "nrReadsNrGenesPerChromPool": "nrReadsNrGenesPerChromPool"
+        ],
+        toState: [
+          "nrReadsNrGenesPerChromPool": "nrReadsNrGenesPerChromPool"
+        ]
+      )
+
+    // The statistics from the STAR logs of different wells are joined
+    // on pool level 
+    star_logs_ch = pool_ch
+      | combine_star_logs.run(
+        directives: ["label": ["lowmem", "verylowcpu"]],
+        fromState: {id, state -> [
+            "star_logs": state.star_output.collect{it.resolve("Log.final.out")},
+            "gene_summary_logs": state.star_output.collect{it.resolve("Solo.out/Gene/Summary.csv")},
+            "reads_per_gene_logs": state.star_output.collect{it.resolve("ReadsPerGene.out.tab")},
+            "barcodes": state.barcode,
+            "output": state.star_qc_metrics
+          ]
+        },
+        toState: [
+          "star_qc_metrics": "output",
+        ]
+      )
+    
+    eset_ch = star_logs_ch.join(pool_statistics_ch, remainder: true)
+      | map {id, star_logs_state, pool_statistics_state ->
+        def newState = star_logs_state + ["nrReadsNrGenesPerChromPool": pool_statistics_state.nrReadsNrGenesPerChromPool]
+        return [id, newState]
+      }
+      | create_pdata.run(
+        directives: [label: ["lowmem", "lowcpu"]],
+        fromState: [
+          "star_stats_file": "star_qc_metrics",
+          "nrReadsNrGenesPerChromPool": "nrReadsNrGenesPerChromPool",
+          "output": "p_data"
+        ],
+        toState: ["p_data": "output"],
+      )
+      | create_eset.run(
+        directives: [label: ["lowmem", "lowcpu"]],
+        fromState: [
+          "pDataFile": "p_data",
+          "fDataFile": "f_data",
+          "mappingDir": "star_output",
+          "output": "eset",
+          "barcodes": "barcode",
+          "poolName": "pool",
+        ],
+        toState: [
+          "eset": "output",
+        ]
+      )
+
+    report_channel = eset_ch
+      | toSortedList()
+      | map {ids_and_states ->
+        def states = ids_and_states.collect{it[1]}
+        def html_report = states[0].html_report
+        def ids = ids_and_states.collect{it[0]}
+        def esets = states.collect{it.eset}
+        ["report", ["esets": esets, "html_report": html_report, "original_ids": ids]]
+      }
+      | create_report.run(
+        fromState: [
+          "eset": "esets",
+          "output_report": "html_report",
+        ],
+        toState: [
+          "html_report": "output_report"
+        ]
+      )
+      | flatMap {id, state ->
+        state.original_ids.collect{original_id ->
+          [original_id, ["html_report": state.html_report]]
+        }
+      }
+
+    output_ch = eset_ch.join(report_channel)
+      | map {id, state_eset, state_report ->
+        def new_state = state_eset + [
+          "html_report": state_report.html_report,
+        ]
+        [id, new_state]
+      }
+      | setState([
+        "star_output": "star_output",
+        "fastq_output": "fastq_output_directory",
+        "nrReadsNrGenesPerChrom": "nrReadsNrGenesPerChromPool",
+        "star_qc_metrics": "star_qc_metrics",
+        "eset": "eset",
+        "f_data": "f_data",
+        "p_data": "p_data",
+        "html_report": "html_report",
+        "run_params": "run_params",
+        "_meta": "_meta",
+      ])
+
+  emit:
+    output_ch
+}
--- a/src/workflows/htrnaseq/nextflow.config
+++ b/src/workflows/htrnaseq/nextflow.config
@@ -0,0 +1,8 @@
+
+params {
+  rootDir = java.nio.file.Paths.get("$projectDir/../../../").toAbsolutePath().normalize().toString()
+}
+
+
+// include common settings
+includeConfig("${params.rootDir}/src/config/labels.config")
--- a/src/workflows/htrnaseq/test.nf
+++ b/src/workflows/htrnaseq/test.nf
@@ -0,0 +1,70 @@
+nextflow.enable.dsl=2
+targetDir = params.rootDir + "/target/nextflow"
+
+include { htrnaseq } from targetDir + "/workflows/htrnaseq/main.nf"
+include { check_eset } from targetDir + "/integration_test_components/htrnaseq/check_eset/main.nf"
+
+
+params.resources_test =  "gs://viash-hub-test-data/htrnaseq/v1/"
+
+workflow test_wf {
+  resources_test_file = file(params.resources_test)
+  input_ch = Channel.fromList([
+      [
+          id: "sample_one",
+          input_r1: resources_test_file.resolve("100k/SRR14730301/VH02001612_S9_R1_001.fastq"),
+          input_r2: resources_test_file.resolve("100k/SRR14730301/VH02001612_S9_R2_001.fastq"),
+          genomeDir: resources_test_file.resolve("genomeDir/gencode.v41.star.sparse"),
+          barcodesFasta: resources_test_file.resolve("360-wells-with-ids.fasta"),
+          annotation: resources_test_file.resolve("genomeDir/gencode.v41.annotation.gtf.gz")
+      ],
+      [
+          id: "sample_two",
+          input_r1: resources_test_file.resolve("100k/SRR14730302/VH02001614_S8_R1_001.fastq"),
+          input_r2: resources_test_file.resolve("100k/SRR14730302/VH02001614_S8_R2_001.fastq"),
+          genomeDir: resources_test_file.resolve("genomeDir/gencode.v41.star.sparse"),
+          barcodesFasta: resources_test_file.resolve("360-wells-with-ids.fasta"),
+          annotation: resources_test_file.resolve("genomeDir/gencode.v41.annotation.gtf.gz")
+      ]
+    ])
+    | map{ state -> [state.id, state] }
+    | view { "Input: $it" }
+    | htrnaseq.run(
+        toState: [
+            "eset": "eset",
+            "star_output": "star_output",
+        ]
+    )
+    | check_eset.run(
+        runIf: {id, state -> id == "sample_one"},
+        toState: [
+            "eset": "eset",
+            "star_output": "star_output"
+        ]
+    )
+}
+
+
+workflow test_wf2 {
+  // Test the edge case where one of the barcodes has no reads
+  resources_test_file = file(params.resources_test)
+  input_ch = Channel.fromList([
+      [
+          id: "sample_one",
+          input_r1: resources_test_file.resolve("100k/SRR14730301/VH02001612_S9_R1_001.fastq"),
+          input_r2: resources_test_file.resolve("100k/SRR14730301/VH02001612_S9_R2_001.fastq"),
+          genomeDir: resources_test_file.resolve("genomeDir/gencode.v41.star.sparse"),
+          barcodesFasta: resources_test_file.resolve("2-wells-1-no-reads.fasta"),
+          annotation: resources_test_file.resolve("genomeDir/gencode.v41.annotation.gtf.gz")
+      ],
+    ])
+    | map{ state -> [state.id, state] }
+    | view { "Input: $it" }
+    | htrnaseq.run(
+        toState: [
+            "eset": "eset",
+            "star_output": "star_output",
+        ]
+    )
+}
+
--- a/src/workflows/runner/config.vsh.yaml
+++ b/src/workflows/runner/config.vsh.yaml
@@ -0,0 +1,124 @@
+name: runner
+namespace: workflows
+description: Runner for HT RNA-seq pipeline
+argument_groups:
+  - name: Input arguments
+    arguments:
+      - name: --input
+        description: |
+          Base directory of the form `s3:/<bucket>/Sequencing/<Sequencer>/<RunID>/<demultiplex_dir>`.
+          Must contains FASTQ files in the format `PoolName_S*_L*_R1_001.fastq.gz` where
+            * PoolName is a unique ID for the microwell plates or combination thereof.
+            * S followed by a running number: the sample number based on the order
+              that samples are listed in the sample sheet (that was used to demultiplex the pools)
+              starting with 1 (e.g. S1)
+            * (Optional) the lane number (e.g. L001)
+            * _001 fixed suffix.
+        type: file
+        required: true
+      - name: --barcodesFasta
+        type: file
+        required: true
+      - name: --genomeDir
+        type: file
+        required: true
+      - name: --annotation
+        type: file
+        required: true
+      - name: --pools
+        description: |
+          Filter the FASTQ files in the input directory to only include pools from the provided list.
+          Pool names are inferred from the FASTQ file names (see input argument for more information).
+          By default all pools are included.
+        type: string
+        multiple: true
+      - name: "--umi_length"
+        description: |
+          Length of the UMI sequences
+        type: integer
+        min: 1
+        default: 10
+      - name: "--run_params"
+        type: file
+        required: false
+        direction: output
+        default: params.yaml
+  - name: Metadata arguments
+    arguments:
+      - name: --id
+        description: Unique identifier for the run
+        type: string
+      - name: --project_id
+        description: Project ID
+        type: string
+        required: true
+      - name: --experiment_id
+        description: Experiment ID
+        type: string
+        required: true
+  - name: Publish arguments
+    arguments:
+      - name: --fastq_publish_dir
+        type: string
+        required: true
+      - name: --results_publish_dir
+        type: string
+        required: true
+  - name: Output arguments
+    arguments:
+      - name: "--star_output_dir"
+        type: file
+        direction: output
+        default: "star_output"
+      - name: "--nrReadsNrGenesPerChrom_dir"
+        type: file
+        direction: output
+        default: "nrReadsNrGenesPerChrom"
+      - name: "--star_qc_metrics_dir"
+        type: file
+        direction: output
+        default: "starLogs"
+      - name: "--eset_dir"
+        type: file
+        direction: output
+        default: "esets"
+      - name: "--f_data_dir"
+        type: file
+        direction: output
+        default: "fData"
+      - name: "--p_data_dir"
+        type: file
+        direction: output
+        default: "pData"
+
+resources:
+  - type: nextflow_script
+    path: main.nf
+    entrypoint: run_wf
+  - path: disable_publishfiles_process.config
+
+test_resources:
+  - type: nextflow_script
+    path: test.nf
+    entrypoint: test_wf
+
+dependencies:
+  - name: utils/listInputDir
+    repository: local
+  - name: workflows/htrnaseq
+    repository: local
+  - name: io/publish_fastqs
+    repository: local
+  - name: io/publish_results
+    repository: local
+  - name: utils/save_params
+    repository: local
+
+runners:
+  - type: nextflow
+    config: 
+      script: 
+        - includeConfig("disable_publishfiles_process.config")
+
+engines:
+  - type: native
--- a/Show More
+++ b/Show More