{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "umap",
  "description": "UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data. Besides tending to be faster than tSNE, it optimizes the embedding such that it best reflects the topology of the data, which we represent throughout Scanpy using a neighborhood graph. tSNE, by contrast, optimizes the distribution of nearest-neighbor distances in the embedding such that these best match the distribution of distances in the high-dimensional space. We use the implementation of umap-learn [McInnes18]. For a few comparisons of UMAP with tSNE, see this preprint.\n",
  "type": "object",
  "$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`, default: `\"rna\"`. ",
          "default": "rna"
        },
        "uns_neighbors": {
          "type": "string",
          "description": "The `.uns` neighbors slot as output by the `find_neighbors` component.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"neighbors\"`. ",
          "default": "neighbors"
        }
      }
    },
    "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 pre/postfix under which to store the UMAP results.",
          "help_text": "Type: `string`, multiple: `False`, default: `\"umap\"`. ",
          "default": "umap"
        },
        "output_compression": {
          "type": "string",
          "description": "Compression format to use for the output AnnData and/or Mudata H5 files.\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": {
        "min_dist": {
          "type": "number",
          "description": "The effective minimum distance between embedded points",
          "help_text": "Type: `double`, multiple: `False`, default: `0.5`. ",
          "default": 0.5
        },
        "spread": {
          "type": "number",
          "description": "The effective scale of embedded points",
          "help_text": "Type: `double`, multiple: `False`, default: `1.0`. ",
          "default": 1.0
        },
        "num_components": {
          "type": "integer",
          "description": "The number of dimensions of the embedding.",
          "help_text": "Type: `integer`, multiple: `False`, default: `2`. ",
          "default": 2
        },
        "max_iter": {
          "type": "integer",
          "description": "The number of iterations (epochs) of the optimization",
          "help_text": "Type: `integer`, multiple: `False`. "
        },
        "alpha": {
          "type": "number",
          "description": "The initial learning rate for the embedding optimization.",
          "help_text": "Type: `double`, multiple: `False`, default: `1.0`. ",
          "default": 1.0
        },
        "gamma": {
          "type": "number",
          "description": "Weighting applied to negative samples in low dimensional embedding optimization",
          "help_text": "Type: `double`, multiple: `False`, default: `1.0`. ",
          "default": 1.0
        },
        "negative_sample_rate": {
          "type": "integer",
          "description": "The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding.",
          "help_text": "Type: `integer`, multiple: `False`, default: `5`. ",
          "default": 5
        },
        "init_pos": {
          "type": "string",
          "description": "How to initialize the low dimensional embedding",
          "help_text": "Type: `string`, multiple: `False`, default: `\"spectral\"`. ",
          "default": "spectral"
        }
      }
    },
    "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"
    }
  ]
}
