GetGroupGeneRanking: Gene Specificity Ranking Calculation

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

Description

Gene Specificity Ranking Calculation

Usage

GetGroupGeneRanking(X, group.by, reduction, dims, features)

## S3 method for class 'Seurat'
GetGroupGeneRanking(X, group.by = NULL,
  reduction = "mca", dims = seq(50), features = NULL)

## S3 method for class 'SingleCellExperiment'
GetGroupGeneRanking(X, group.by,
  reduction = "MCA", dims = seq(50), features = NULL)

Arguments

X	Seurat or SingleCellExperiment object, alternatively a matrix.
group.by	column name of meta.data (Seurat) or ColData (SingleCellExperiment)
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	A character vector of features name to subset feature coordinates for distance calculation.

Value

List of genes ranking for each groups

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.