{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "tsne",
  "description": "t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique used to visualize high-dimensional data in a low-dimensional space, revealing patterns and clusters by preserving local data similarities.\n",
  "type": "object",
  "$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`, example: `\"input.h5mu\"`. "
        },
        "modality": {
          "type": "string",
          "description": "Which modality from the input MuData file to process.\n",
          "help_text": "Type: `string`, multiple: `False`, required, default: `\"rna\"`. ",
          "default": "rna"
        },
        "use_rep": {
          "type": "string",
          "description": "The `.obsm` slot to use as input for the tSNE computation.",
          "help_text": "Type: `string`, multiple: `False`, required, example: `\"X_pca\"`. "
        }
      }
    },
    "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.h5mu\"`, direction: `output`, example: `\"output.h5mu\"`. ",
          "default": "$id.$key.output.h5mu"
        },
        "obsm_output": {
          "type": "string",
          "description": "The .obsm key to use for storing the tSNE results.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"X_tsne\"`. ",
          "default": "X_tsne"
        },
        "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"
          ]
        }
      }
    },
    "arguments": {
      "title": "Arguments",
      "type": "object",
      "description": "No description",
      "properties": {
        "n_pcs": {
          "type": "integer",
          "description": "The number of principal components to use for the tSNE computation.",
          "help_text": "Type: `integer`, multiple: `False`, default: `50`. ",
          "default": 50
        },
        "perplexity": {
          "type": "number",
          "description": "The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms",
          "help_text": "Type: `double`, multiple: `False`, default: `30.0`. ",
          "default": 30.0
        },
        "min_dist": {
          "type": "number",
          "description": "The effective minimum distance between embedded points",
          "help_text": "Type: `double`, multiple: `False`, default: `0.5`. ",
          "default": 0.5
        },
        "metric": {
          "type": "string",
          "description": "Distance metric to calculate neighbors on.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"euclidean\"`. ",
          "default": "euclidean"
        },
        "early_exaggeration": {
          "type": "number",
          "description": "Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them",
          "help_text": "Type: `double`, multiple: `False`, default: `12.0`. ",
          "default": 12.0
        },
        "learning_rate": {
          "type": "number",
          "description": "The learning rate for t-SNE optimization",
          "help_text": "Type: `double`, multiple: `False`, default: `1000.0`. ",
          "default": 1000.0
        },
        "random_state": {
          "type": "integer",
          "description": "The random seed to use for the tSNE computation.",
          "help_text": "Type: `integer`, multiple: `False`, default: `0`. ",
          "default": 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/arguments"
    },
    {
      "$ref": "#/$defs/nextflow input-output arguments"
    }
  ]
}
