filterByExpr: Filter Genes By Expression Level

In hiraksarkar/edgeR_fork: Empirical Analysis of Digital Gene Expression Data in R

Description

Determine which genes have sufficiently large counts to be retained in a statistical analysis.

Usage

## S3 method for class 'DGEList'
filterByExpr(y, design = NULL, group = NULL, lib.size = NULL, ...)
## S3 method for class 'SummarizedExperiment'
filterByExpr(y, design = NULL, group = NULL, lib.size = NULL, ...)
## Default S3 method:
filterByExpr(y, design = NULL, group = NULL, lib.size = NULL,
             min.count = 10, min.total.count = 15, large.n = 10, min.prop = 0.7, ...)

Arguments

y	matrix of counts, or a DGEList object, or a SummarizedExperiment object.
design	design matrix. Ignored if group is not NULL.
group	vector or factor giving group membership for a oneway layout, if appropriate.
lib.size	library size, defaults to colSums(y).
min.count	numeric. Minimum count required for at least some samples.
min.total.count	numeric. Minimum total count required.
large.n	integer. Number of samples per group that is considered to be “large”.
min.prop	numeric. Minimum proportion of samples in the smallest group that express the gene.
...	any other arguments. For the DGEList and SummarizedExperiment methods, other arguments will be passed to the default method. For the default method, other arguments are not currently used.

Details

This function implements the filtering strategy that was intuitively described by Chen et al (2016). Roughly speaking, the strategy keeps genes that have at least min.count reads in a worthwhile number samples. More precisely, the filtering keeps genes that have count-per-million (CPM) above k in n samples, where k is determined by min.count and by the sample library sizes and n is determined by the design matrix.

n is essentially the smallest group sample size or, more generally, the minimum inverse leverage of any fitted value. If all the group sizes are larger than large.n, then this is relaxed slightly, but with n always greater than min.prop of the smallest group size (70% by default).

In addition, each kept gene is required to have at least min.total.count reads across all the samples.

Value

Logical vector of length nrow(y) indicating which rows of y to keep in the analysis.

Author(s)

Gordon Smyth

References

Chen Y, Lun ATL, and Smyth, GK (2016). From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5, 1438. http://f1000research.com/articles/5-1438

Examples

## Not run: 
keep <- filterByExpr(y, design)
y <- y[keep,]

## End(Not run)

hiraksarkar/edgeR_fork documentation built on Dec. 20, 2021, 3:52 p.m.