getCounts

getOffset

getDispersion

<code>DGEList</code> object containing (at least) the elements <code>counts</code> (table of raw counts), <code>group</code> (factor indicating group) and <code>lib.size</code> (numeric vector of library sizes)

<code>getCounts(y)</code> returns the matrix of read counts <code>y$counts</code>.<code>getOffset(y)</code> returns offsets for the log-linear predictor account for sequencing depth and possibly other normalization factors.
Specifically it returns the matrix <code>y$offset</code> if it is non-null, otherwise it returns the log product of <code>lib.size</code> and <code>norm.factors</code> from <code>y$samples</code>.<code>getDispersion(y)</code> returns the most complex dispersion estimates (common, trended or genewise) found in <code>y</code>.

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.

Yunshun Chen

edgeR

Empirical Analysis of Digital Gene Expression Data in R

getCounts function

<code>getCounts(y)</code> returns the matrix of read counts <code>y$counts</code>.

<code>getOffset(y)</code> returns offsets for the log-linear predictor account for sequencing depth and possibly other normalization factors.
Specifically it returns the matrix <code>y$offset</code> if it is non-null, otherwise it returns the log product of <code>lib.size</code> and <code>norm.factors</code> from <code>y$samples</code>.

<code>getDispersion(y)</code> returns the most complex dispersion estimates (common, trended or genewise) found in <code>y</code>.

getCounts: Extract Specified Component of a DGEList Object

Description

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See Also

Examples