CI
5b8fa21d13
Build pipeline: openpipelines-bio.openpipeline.processing-workflows-quantijd49m
Source commit: 38f5f83d11
Source message: fix tests
59 lines
2.2 KiB
Bash
Executable File
59 lines
2.2 KiB
Bash
Executable File
#!/bin/bash
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set -eo pipefail
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# get the root of the directory
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REPO_ROOT=$(git rev-parse --show-toplevel)
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# ensure that the command below is run from the root of the repository
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cd "$REPO_ROOT"
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ID=HLCA_reference_model
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OUT=resources_test/$ID/$ID
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DIR=$(dirname "$OUT")
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# ideally, this would be a versioned pipeline run
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[ -d "$DIR" ] || mkdir -p "$DIR"
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# download and unarchive pre-trained scANVI model
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wget https://zenodo.org/record/6337966/files/HLCA_reference_model.zip \
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-O "${OUT}.zip"
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# # Test query data
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# # Source publication: Delorey, Toni M., et al. “COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.” Nature 595.7865 (2021): 107-113.
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# wget https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5230nnn/GSM5230027/suppl/GSM5230027_04-P103142-S149-R01_raw_feature_bc_matrix.h5.gz \
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# -O "${OUT}_query_test.h5.gz"
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# gzip -d "${OUT}_query_test.h5.gz"
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# # Prepare test data as in scvi-tools tutorial: https://docs.scvi-tools.org/en/stable/tutorials/notebooks/query_hlca_knn.html
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# python <<HEREDOC
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# import pandas as pd
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# import scanpy as sc
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# geo_metadata_url = "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE171nnn/GSE171668/suppl/GSE171668_lung_metadata.csv.gz"
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# metadata = pd.read_csv(geo_metadata_url, index_col=0)
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# DATA_PATH = "${OUT}_query_test.h5"
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# query_data = sc.read_10x_h5(DATA_PATH)
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# # clean up .var.index (gene names)
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# query_data.var['gene_names'] = query_data.var.index
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# query_data.var.index = [idx.split("___")[-1] for idx in query_data.var.gene_ids]
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# # clean up cell barcodes:
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# query_data.obs.index = query_data.obs.index.str.rstrip("-1")
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# # read in metadata (to select only cells of interest and remove empty drops)
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# # subset to cells from our sample
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# metadata = metadata.loc[metadata.donor == "D12_4",:].copy()
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# # clean up barcodes:
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# metadata.index = [idx.split("-")[-1] for idx in metadata.index]
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# # subset adata to cells in metadata:
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# query_data = query_data[metadata.index,:].copy()
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# # add dataset information:
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# query_data.obs['dataset'] = "test_dataset_delorey_regev"
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# sc.write(DATA_PATH, query_data)
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# HEREDOC
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# # convert 10x h5 to h5mu
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# viash run src/convert/from_h5ad_to_h5mu/config.vsh.yaml -- \
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# --input "${OUT}_query_test.h5" \
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# --output "${OUT}_query_test.h5mu"
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