GetCellGeneSet: Gene sets extraction from MCA

In cbl-imagine/CellID: Unbiased Extraction of Single Cell Identity using Multiple Correspondence Analysis

Description

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

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) 

cbl-imagine/CellID documentation built on July 22, 2020, 7:18 p.m.