Brennecke_getVariableGenes: Identify Highly Variable Genes

In tallulandrews/M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Identify Highly Variable Genes

Description

Implements the method of Brennecke et al. (2013) to identify highly variable genes.

Usage

BrenneckeGetVariableGenes(expr_mat, spikes=NA, suppress.plot=FALSE, fdr=0.1, minBiolDisp=0.5, fitMeanQuantile=0.8)

Arguments

expr_mat	a numeric matrix of normalized or raw (not log-transformed) expression values, columns = samples, rows = genes.
spikes	a vector of gene names of row numbers of spike-in genes which are subject to only technical variance.
suppress.plot	Whether to make the plot or just calculate the requisite values.
fdr	Use FDR to identify significantly highly variable genes.
minBiolDisp	Minimum percentage of variance due to biological factors.
fitMeanQuantile	Threshold for genes to be used in fitting. May need to be decreased in low-depth/umi-tagged datasets to achieve good fit.

Details

Identifies significantly highly variable genes as detailed in Brennecked et al [1]. If spike-ins are provided they are used fit a function to the relationship between gene expression and variance due to technical factors. If spike-ins are not provided then all genes are used in the fitting.

Value

Vector of names of highly variable genes.

References

Brennecke et al. (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nature Methods 10, 1093-1095. doi:10.1038/nmeth.2645

Examples

  library(M3DExampleData)
  HVG <- BrenneckeGetVariableGenes(Mmus_example_list$data)
  HVG_spike <- BrenneckeGetVariableGenes(Mmus_example_list$data, spikes=5550:5600)

tallulandrews/M3Drop documentation built on March 6, 2024, 1:49 a.m.