GetCellGeneSet: Gene sets extraction from MCA

Description Usage Arguments Value Examples

View source: R/cell.R

Description

Calculate cells and genes distances, rank them per cell and extract top n features

Usage

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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

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

Cortalak/cellID documentation built on Aug. 3, 2020, 9:01 p.m.