Filter Genes By Expression Level
Determine which genes have sufficiently large counts to be retained in a statistical analysis.
## 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, ...)y |
matrix of counts, or a |
design |
design matrix. Ignored if |
group |
vector or factor giving group membership for a oneway layout, if appropriate. |
lib.size |
library size, defaults to |
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 |
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.
Logical vector of length nrow(y) indicating which rows of y to keep in the analysis.
Gordon Smyth
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
## Not run: keep <- filterByExpr(y, design) y <- y[keep,] ## End(Not run)
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