pca

pca

Perform principal component analysis on single-cell RNA sequencing data to reduce dimensionality, extract key patterns, and enable efficient visualization and analysis.

Instructions

Principal component analysis

Input Schema

Name	Required	Description	Default
`chunk_size`	No	Number of observations to include in each chunk.
`chunked`	No	If True, perform an incremental PCA on segments.
`dtype`	No	Numpy data type string for the result.	float32
`layer`	No	If provided, which element of layers to use for PCA.
`mask_var`	No	Boolean mask or string referring to var column for subsetting genes.
`n_comps`	No	Number of principal components to compute. Defaults to 50 or 1 - minimum dimension size.
`svd_solver`	No	SVD solver to use.
`zero_center`	No	If True, compute standard PCA from covariance matrix.

Input Schema (JSON Schema)

{
  "description": "Input schema for the PCA preprocessing tool.",
  "properties": {
    "chunk_size": {
      "anyOf": [
        {
          "exclusiveMinimum": 0,
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Number of observations to include in each chunk.",
      "title": "Chunk Size"
    },
    "chunked": {
      "default": false,
      "description": "If True, perform an incremental PCA on segments.",
      "title": "Chunked",
      "type": "boolean"
    },
    "dtype": {
      "default": "float32",
      "description": "Numpy data type string for the result.",
      "title": "Dtype",
      "type": "string"
    },
    "layer": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "If provided, which element of layers to use for PCA.",
      "title": "Layer"
    },
    "mask_var": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Boolean mask or string referring to var column for subsetting genes.",
      "title": "Mask Var"
    },
    "n_comps": {
      "anyOf": [
        {
          "exclusiveMinimum": 0,
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Number of principal components to compute. Defaults to 50 or 1 - minimum dimension size.",
      "title": "N Comps"
    },
    "svd_solver": {
      "anyOf": [
        {
          "enum": [
            "arpack",
            "randomized",
            "auto",
            "lobpcg",
            "tsqr"
          ],
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "SVD solver to use.",
      "title": "Svd Solver"
    },
    "zero_center": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "default": true,
      "description": "If True, compute standard PCA from covariance matrix.",
      "title": "Zero Center"
    }
  },
  "title": "PCAModel",
  "type": "object"
}

SCMCP

Instructions

Input Schema

Input Schema (JSON Schema)

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