{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "scanvi",
  "description": "scANVI () is a semi-supervised model for single-cell transcriptomics data. scANVI is an scVI extension that can leverage the cell type knowledge for a subset of the cells present in the data sets to infer the states of the rest of the cells.\nThis component will instantiate a scANVI model from a pre-trained scVI model, integrate the data and perform label prediction.\n",
  "type": "object",
  "$defs": {
    "inputs": {
      "title": "Inputs",
      "type": "object",
      "description": "No description",
      "properties": {
        "input": {
          "type": "string",
          "format": "path",
          "exists": true,
          "description": "Input h5mu file",
          "help_text": "Type: `file`, multiple: `False`, required, direction: `input`. "
        },
        "modality": {
          "type": "string",
          "description": "Which modality from the input MuData file to process.\n",
          "help_text": "Type: `string`, multiple: `False`, default: `\"rna\"`. ",
          "default": "rna"
        },
        "input_layer": {
          "type": "string",
          "description": "Input layer to use",
          "help_text": "Type: `string`, multiple: `False`. "
        },
        "var_input": {
          "type": "string",
          "description": ".var column containing highly variable genes that were used to train the scVi model",
          "help_text": "Type: `string`, multiple: `False`. "
        },
        "var_gene_names": {
          "type": "string",
          "description": ".var column containing gene names",
          "help_text": "Type: `string`, multiple: `False`. "
        },
        "obs_labels": {
          "type": "string",
          "description": ".obs field containing the labels",
          "help_text": "Type: `string`, multiple: `False`, required. "
        },
        "unlabeled_category": {
          "type": "string",
          "description": "Value in the --obs_labels field that indicates unlabeled observations\n",
          "help_text": "Type: `string`, multiple: `False`, default: `\"Unknown\"`. ",
          "default": "Unknown"
        }
      }
    },
    "outputs": {
      "title": "Outputs",
      "type": "object",
      "description": "No description",
      "properties": {
        "output": {
          "type": "string",
          "format": "path",
          "description": "Output h5mu file.",
          "help_text": "Type: `file`, multiple: `False`, required, default: `\"$id.$key.output\"`, direction: `output`. ",
          "default": "$id.$key.output"
        },
        "output_model": {
          "type": "string",
          "format": "path",
          "description": "Folder where the state of the trained model will be saved to.",
          "help_text": "Type: `file`, multiple: `False`, default: `\"$id.$key.output_model\"`, direction: `output`. ",
          "default": "$id.$key.output_model"
        },
        "obsm_output": {
          "type": "string",
          "description": "In which .obsm slot to store the resulting integrated embedding.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"X_scanvi_integrated\"`. ",
          "default": "X_scanvi_integrated"
        },
        "obs_output_predictions": {
          "type": "string",
          "description": "In which .obs slot to store the predicted labels.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"scanvi_pred\"`. ",
          "default": "scanvi_pred"
        },
        "obs_output_probabilities": {
          "type": "string",
          "description": "In which",
          "help_text": "Type: `string`, multiple: `False`, default: `\"scanvi_proba\"`. ",
          "default": "scanvi_proba"
        },
        "output_compression": {
          "type": "string",
          "description": "Compression format to use for the output AnnData and/or Mudata objects.\nBy default no compression is applied.\n",
          "help_text": "Type: `string`, multiple: `False`, example: `\"gzip\"`, choices: ``gzip`, `lzf``. ",
          "enum": [
            "gzip",
            "lzf"
          ]
        }
      }
    },
    "scvi model": {
      "title": "scVI Model",
      "type": "object",
      "description": "No description",
      "properties": {
        "scvi_model": {
          "type": "string",
          "format": "path",
          "exists": true,
          "description": "Pretrained SCVI reference model to initialize the SCANVI model with.",
          "help_text": "Type: `file`, multiple: `False`, required, direction: `input`, example: `\"scvi_model.pt\"`. "
        }
      }
    },
    "scanvi training arguments": {
      "title": "scANVI training arguments",
      "type": "object",
      "description": "No description",
      "properties": {
        "early_stopping": {
          "type": "boolean",
          "description": "Whether to perform early stopping with respect to the validation set.",
          "help_text": "Type: `boolean`, multiple: `False`. "
        },
        "early_stopping_monitor": {
          "type": "string",
          "description": "Metric logged during validation set epoch.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"elbo_validation\"`, choices: ``elbo_validation`, `reconstruction_loss_validation`, `kl_local_validation``. ",
          "enum": [
            "elbo_validation",
            "reconstruction_loss_validation",
            "kl_local_validation"
          ],
          "default": "elbo_validation"
        },
        "early_stopping_patience": {
          "type": "integer",
          "description": "Number of validation epochs with no improvement after which training will be stopped.",
          "help_text": "Type: `integer`, multiple: `False`, default: `45`. ",
          "default": 45
        },
        "early_stopping_min_delta": {
          "type": "number",
          "description": "Minimum change in the monitored quantity to qualify as an improvement, i.e",
          "help_text": "Type: `double`, multiple: `False`, default: `0.0`. ",
          "default": 0.0
        },
        "max_epochs": {
          "type": "integer",
          "description": "Number of passes through the dataset, defaults to (20000 / number of cells) * 400 or 400; whichever is smallest.",
          "help_text": "Type: `integer`, multiple: `False`. "
        },
        "reduce_lr_on_plateau": {
          "type": "boolean",
          "description": "Whether to monitor validation loss and reduce learning rate when validation set `lr_scheduler_metric` plateaus.",
          "help_text": "Type: `boolean`, multiple: `False`, default: `true`. ",
          "default": true
        },
        "lr_factor": {
          "type": "number",
          "description": "Factor to reduce learning rate.",
          "help_text": "Type: `double`, multiple: `False`, default: `0.6`. ",
          "default": 0.6
        },
        "lr_patience": {
          "type": "number",
          "description": "Number of epochs with no improvement after which learning rate will be reduced.",
          "help_text": "Type: `double`, multiple: `False`, default: `30.0`. ",
          "default": 30.0
        }
      }
    },
    "nextflow input-output arguments": {
      "title": "Nextflow input-output arguments",
      "type": "object",
      "description": "Input/output parameters for Nextflow itself. Please note that both publishDir and publish_dir are supported but at least one has to be configured.",
      "properties": {
        "publish_dir": {
          "type": "string",
          "description": "Path to an output directory.",
          "help_text": "Type: `string`, multiple: `False`, required, example: `\"output/\"`. "
        }
      }
    }
  },
  "allOf": [
    {
      "$ref": "#/$defs/inputs"
    },
    {
      "$ref": "#/$defs/outputs"
    },
    {
      "$ref": "#/$defs/scvi model"
    },
    {
      "$ref": "#/$defs/scanvi training arguments"
    },
    {
      "$ref": "#/$defs/nextflow input-output arguments"
    }
  ]
}
