GetCellGeneSet {CelliD} | R Documentation

## Gene sets extraction from MCA

### Description

Calculate cells and genes distances, rank them per cell and extract top n features. The obtained top n features represents features thatare highly specific to that cell.

### Usage

    GetCellGeneSet(X, reduction = "mca", dims, features, cells, n.features)

    ## S3 method for class 'Seurat'
    GetCellGeneSet(
      X,
      reduction = "mca",
      dims = seq(50),
      features = NULL,
      cells = NULL,
      n.features = 200
    )

    ## S3 method for class 'SingleCellExperiment'
    GetCellGeneSet(
      X,
      reduction = "MCA",
      dims = seq(50),
      features = NULL,
      cells = NULL,
      n.features = 200
    )

### Arguments

`X` | Seurat or SingleCell Experiment Object
`reduction` | Which dimensionality reduction to use, must be based on MCA.
`dims` | A vector of integers indicating which dimensions to use with reduction embeddings and loadings for distance calculation.
`features` | Character vector of feature names to subset feature coordinates. If not specified will take all features available from specified reduction Loadings
`cells` | Character vector of cell names to subset cell coordinates. If not specified will take all features available from specified reduction Embeddigns.
`n.features` | single integer specifying how many top features should be extracted from the ranking

### Value

A cell named list of gene rankings ordererd by distances from shortest (most specfic) to farthest (less specific)

### Examples

    seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)
    GroupGeneRanking <- GetGroupGeneRanking(seuratPbmc, group.by = "seurat_clusters", dims = 1:5)

[Package CelliD version 1.2.1 Index]