GLMPCA {scry}R Documentation

Generalized principal components analysis for non-normally distributed data

Description

This function implements the GLM-PCA dimensionality reduction method for high-dimensional count data. This is a wrapper for glmpca.

Usage

GLMPCA(object, ...)

## S4 method for signature 'SummarizedExperiment'
GLMPCA(object, assay = 1, L, ...)

## S4 method for signature 'matrix'
GLMPCA(object, assay = 1, L, ...)

## S4 method for signature 'Matrix'
GLMPCA(object, assay = 1, L, ...)

Arguments

object

A SingleCellExperiment or SummarizedExperiment object. Alternatively, a matrix of integer counts.

...

further arguments passed to glmpca

assay

a character or integer specifying which assay to use for GLM-PCA (default = 1). Ignored if object is a matrix.

L

the desired number of latent dimensions (integer).

Value

The original SingleCellExperiment or SummarizedExperiment object with the GLM-PCA results added to the metadata slot. If the original input was a SingleCellExperiment, then a new reducedDim element called "GLMPCA" will be added, representing the GLM-PCA factors. If the input was a matrix, output matches that of glmpca.

Examples

ncells <- 100
u <- matrix(rpois(20000, 5), ncol=ncells)
sce <- SingleCellExperiment::SingleCellExperiment(assays=list(counts=u))
GLMPCA(sce, L = 2)


[Package scry version 1.0.1 Index]