umap

umap

Visualize and reduce high-dimensional data into lower dimensions using uniform manifold approximation and projection (UMAP) for enhanced analysis, tailored for single-cell RNA sequencing workflows.

Instructions

Uniform Manifold Approximation and Projection (UMAP) for visualization

Input Schema

Name	Required	Description	Default
`a`	No	Parameter controlling the embedding.
`alpha`	No	Initial learning rate for the embedding optimization.
`b`	No	Parameter controlling the embedding.
`gamma`	No	Weighting applied to negative samples.
`init_pos`	No	How to initialize the low dimensional embedding.	spectral
`maxiter`	No	Number of iterations (epochs) of the optimization.
`method`	No	Implementation to use ('umap' or 'rapids').	umap
`min_dist`	No	Minimum distance between embedded points.
`n_components`	No	Number of dimensions of the embedding.
`negative_sample_rate`	No	Number of negative samples per positive sample.
`neighbors_key`	No	Key for neighbors settings in .uns.
`random_state`	No	Random seed for reproducibility.
`spread`	No	Scale of embedded points.

Input Schema (JSON Schema)

{
  "description": "Input schema for the UMAP dimensionality reduction tool.",
  "properties": {
    "a": {
      "anyOf": [
        {
          "exclusiveMinimum": 0,
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Parameter controlling the embedding.",
      "title": "A"
    },
    "alpha": {
      "default": 1,
      "description": "Initial learning rate for the embedding optimization.",
      "exclusiveMinimum": 0,
      "title": "Alpha",
      "type": "number"
    },
    "b": {
      "anyOf": [
        {
          "exclusiveMinimum": 0,
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Parameter controlling the embedding.",
      "title": "B"
    },
    "gamma": {
      "default": 1,
      "description": "Weighting applied to negative samples.",
      "exclusiveMinimum": 0,
      "title": "Gamma",
      "type": "number"
    },
    "init_pos": {
      "default": "spectral",
      "description": "How to initialize the low dimensional embedding.",
      "title": "Init Pos",
      "type": "string"
    },
    "maxiter": {
      "anyOf": [
        {
          "exclusiveMinimum": 0,
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Number of iterations (epochs) of the optimization.",
      "title": "Maxiter"
    },
    "method": {
      "default": "umap",
      "description": "Implementation to use ('umap' or 'rapids').",
      "title": "Method",
      "type": "string"
    },
    "min_dist": {
      "default": 0.5,
      "description": "Minimum distance between embedded points.",
      "exclusiveMinimum": 0,
      "title": "Min Dist",
      "type": "number"
    },
    "n_components": {
      "default": 2,
      "description": "Number of dimensions of the embedding.",
      "exclusiveMinimum": 0,
      "title": "N Components",
      "type": "integer"
    },
    "negative_sample_rate": {
      "default": 5,
      "description": "Number of negative samples per positive sample.",
      "exclusiveMinimum": 0,
      "title": "Negative Sample Rate",
      "type": "integer"
    },
    "neighbors_key": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Key for neighbors settings in .uns.",
      "title": "Neighbors Key"
    },
    "random_state": {
      "default": 0,
      "description": "Random seed for reproducibility.",
      "title": "Random State",
      "type": "integer"
    },
    "spread": {
      "default": 1,
      "description": "Scale of embedded points.",
      "exclusiveMinimum": 0,
      "title": "Spread",
      "type": "number"
    }
  },
  "title": "UMAPModel",
  "type": "object"
}

SCMCP

Instructions

Input Schema

Input Schema (JSON Schema)

Other Tools from SCMCP

Related Tools

MCP directory API