de.ana {EnrichmentBrowser} | R Documentation |
The function carries out a differential expression analysis between two sample groups. Resulting fold changes and derived p-values are returned. Raw p-values are corrected for multiple testing.
de.ana( expr, grp = NULL, blk = NULL, de.method = c("limma", "edgeR", "DESeq"), padj.method = "BH", stat.only=FALSE, min.cpm=2 )
expr |
Expression data.
A numeric matrix. Rows correspond to genes, columns to samples.
Alternatively, this can also be an object of class
|
grp |
*BINARY* group assignment for the samples.
Use '0' and '1' for unaffected (controls) and affected (cases)
samples, respectively.
If NULL, this is assumed to be defined via a column named 'GROUP'
in the |
blk |
Optional. For paired samples or sample blocks.
This can also be defined via a column named 'BLOCK'
in the |
de.method |
Differential expression method. Use 'limma' for microarray and RNA-seq data. Alternatively, differential expression for RNA-seq data can be also calculated using edgeR ('edgeR') or DESeq2 ('DESeq'). Defaults to 'limma'. |
padj.method |
Method for adjusting p-values to multiple testing.
For available methods see the man page of the
stats function |
stat.only |
Logical. Should only the test statistic be returned? This is mainly for internal use, in order to carry out permutation tests on the DE statistic for each gene. Defaults to FALSE. |
min.cpm |
In case of RNA-seq data: should genes not satisfying a minimum counts-per-million (cpm) threshold be excluded from the analysis? This is typically recommended. See the edgeR vignette for details. The default filter is to exclude genes with cpm < 2 in more than half of the samples. |
A DE-table with measures of differential expression for each gene/row,
i.e. a two-column matrix with log2 fold changes in the 1st column
and derived p-values in the 2nd column.
If 'expr' is a SummarizedExperiment
, the DE-table
will be automatically appended to the rowData
slot.
Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>
read.eset
for reading expression data from file,
normalize
for normalization of expression data,
voom
for preprocessing of RNA-seq data,
p.adjust
for multiple testing correction,
eBayes
for DE analysis with limma,
glmFit
for DE analysis with edgeR, and
DESeq
for DE analysis with DESeq.
# (1) microarray data: intensity measurements ma.eset <- make.example.data(what="eset", type="ma") ma.eset <- de.ana(ma.eset) rowData(ma.eset, use.names=TRUE) # (2) RNA-seq data: read counts rseq.eset <- make.example.data(what="eset", type="rseq") rseq.eset <- de.ana(rseq.eset, de.method="DESeq") rowData(rseq.eset, use.names=TRUE)