Brennecke_getVariableGenes: Identify Highly Variable Genes
In tallulandrews/M3D: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Description Usage Arguments Details Value References Examples

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

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

`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.

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.

Vector of names of highly variable genes.

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

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