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.
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Expression data. A numeric matrix. Rows correspond to genes,
columns to samples. Alternatively, this can also be an object of class
*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
Optional. For paired samples or sample blocks. This can also be
defined via a column named 'BLOCK' in the
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 ('DESeq2'). Defaults
Method for adjusting p-values to multiple testing. For
available methods see the man page of the stats function
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
Logical. For RNA-seq data: include only genes with
sufficiently large counts in the DE analysis? If TRUE, excludes genes not
satisfying a minimum number of read counts across samples using the
Character. The name of the assay for differential expression
SummarizedExperiment with *multiple assays*:
For the typical use case within the EnrichmentBrowser workflow this will
SummarizedExperiment with two assays: (i) an assay
storing the *raw* expression values, and (ii) an assay storing the *norm*alized
expression values as obtained with the
In this case,
assay = "auto" will *auto*matically determine the assay
based on the data type provided. For microarray data, differential expression
analysis will be carried out on the assay storing the *norm*alized log2 intensities.
For RNA-seq data, differential expression analysis will be carried out on the
assay storing the *raw* read counts.
For usage outside of the typical workflow, the
assay argument can be
used to provide the name of the assay for differential expression analysis.
For differential expression analysis of microarray data with
de.method = "limma", this assay should contain the *norm*alized log2
intensities. For differential expression analysis of RNA-seq data with either
method (limma/voom, edgeR, or DESeq2), the specified assay should contain the
*raw* read counts.
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
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,
for multiple testing correction,
eBayes for DE analysis with
glmQLFit for DE analysis with edgeR, and
DESeq for DE analysis with DESeq2.
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