BrenneckeGetVariableGenes {M3Drop} | R Documentation

## 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)

[Package M3Drop version 1.12.0 Index]