RunMCA: Run Multiple Correspondence Analysis

In RausellLab/CelliD: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis

Run Multiple Correspondence Analysis

Description

RunMCA allows to compute the Multiple Corespondence Analysis on the single cell data contained in Seurat or SingleCellExperiment. MCA is a statistical technique close to PCA that provides a simultaneous representation of observations (e.g. cells) and variables (e.g. genes) in low-dimensional space. The barycentric relation among cells and genes is a distinctive feature of MCA biplots and represents a major advantage as compared to other types of biplots such as those produced by Principal Component Analysis as well as over alternative low-dimensional transformations providing only cell projections. Thus, in the MCA biplot, analytical distances can be calculated not only between cells and between genes, but also between each cell and each gene in order to estimate its association. Thus, the closer a gene g is to a cell c, the more specific to such a cell it can be considered. Gene-to-cell distances can then be ranked for each individual cell, and the top-ranked genes may be regarded as a unique gene signature representing the identity card of the cell.

Usage

RunMCA(X, nmcs, features, reduction.name, slot, ...)

## S3 method for class 'matrix'
RunMCA(X, nmcs = 50, features = NULL, reduction.name = "MCA", ...)

## S3 method for class 'Seurat'
RunMCA(
  X,
  nmcs = 50,
  features = NULL,
  reduction.name = "mca",
  slot = "data",
  assay = DefaultAssay(X),
  ...
)

## S3 method for class 'SingleCellExperiment'
RunMCA(
  X,
  nmcs = 50,
  features = NULL,
  reduction.name = "MCA",
  slot = "logcounts",
  ...
)

Arguments

X	Seurat, SingleCellExperiment or matrix object
nmcs	number of components to compute and store, default set to 30
features	character vector of feature names. If not specified all features will be taken.
reduction.name	name of the reduction default set to 'MCA' for SingleCellExperiment and mca
slot	Which slot to pull expression data from? Default to logcounts for SingleCellExperiment and data for Seurat.
...	other aruments passed to methods
assay	Name of Assay MCA is being run on

Value

Seurat or SCE object with MCA calculation stored in the reductions slot.

Examples

seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)

RausellLab/CelliD documentation built on Jan. 12, 2024, 3:44 a.m.