filterCounts: Count data filtering

In tweeDEseq: RNA-seq data analysis using the Poisson-Tweedie family of distributions

Description

Filter count data to remove lowly expressed genes.

Usage

filterCounts(counts, cpm.cutoff=0.5, n.samples.cutoff=2, mean.cpm.cutoff=0, lib.sizes=NULL)

Arguments

counts	numeric data.frame or matrix containing the count data.
cpm.cutoff	expression level cutoff defined as the minimum number of counts per million. By default this is set to 0.5 counts per million.
n.samples.cutoff	minimum number of samples where a gene should meet the counts per million cutoff (cpm.cutoff) in order to be kept as part of the count data matrix. When n.samples.cutoff is a number between 0 and 1, then it is interpreted as the fraction of samples that should meet the counts per million cutoff (cpm.cutoff).
mean.cpm.cutoff	minimum mean of counts per million cutoff that a gene should meet in order to be kept. When the value of this argument is larger than 0 then it overrules the other arguments cpm.cutoff and n.samples.cutoff.
lib.sizes	vector of the total number of reads to be considered per sample/library. If lib.sizes=NULL (default) then these quantities are estimated as the column sums in the input matrix of counts.

Details

This function removes genes with very low expression level defined in terms of a minimum number of counts per million occurring in a minimum number of samples. Such a policy was described by Davis McCarthy in a message at the bioc-sig-sequencing mailing list. By default, this function keeps genes that are expressed at a level of 0.5 counts per million or greater in at least two samples. Alternatively, one can use the mean.cpm.cutoff to set a minimum mean expression level through all the samples.

Value

A matrix of filtered genes.

Author(s)

J.R. Gonzalez and R. Castelo

References

Davis McCarthy, https://stat.ethz.ch/pipermail/bioc-sig-sequencing/2011-June/002072.html.

See Also

normalizeCounts

Examples

# Generate a random matrix of counts
counts <- matrix(rPT(n=1000, a=0.5, mu=10, D=5), ncol = 40)

dim(counts)

# Filter genes with requiring the minimum expression level on every sample
filteredCounts <- filterCounts(counts, n.samples.cutoff=dim(counts)[2])

dim(filteredCounts)

Example output

[1] 25 40
[1] 17 40

tweeDEseq documentation built on Nov. 8, 2020, 5:59 p.m.