deAna {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.
deAna(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>
readSE
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 maSE <- makeExampleData(what="SE", type="ma") maSE <- deAna(maSE) rowData(maSE, use.names=TRUE) # (2) RNA-seq data: read counts rseqSE <- makeExampleData(what="SE", type="rseq") rseqSE <- deAna(rseqSE, de.method="DESeq") rowData(rseqSE, use.names=TRUE)