deAna {EnrichmentBrowser} | R Documentation

## Differential expression analysis between two sample groups

### Description

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.

### Usage

    deAna(expr, grp = NULL, blk = NULL, de.method = c("limma", "edgeR",
      "DESeq2"), padj.method = "BH", stat.only = FALSE)

### Arguments

`expr` | Expression data. A numeric matrix. Rows correspond to genes, columns to samples. Alternatively, this can also be an object of class `SummarizedExperiment`.
`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 `colData` slot if 'expr' is a `SummarizedExperiment`.
`blk` | Optional. For paired samples or sample blocks. This can also be defined via a column named 'BLOCK' in the `colData` slot if 'expr' is a `SummarizedExperiment`.
`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 ('DESeq2'). Defaults to 'limma'.
`padj.method` | Method for adjusting p-values to multiple testing. For available methods see the man page of the stats function `p.adjust`. Defaults to 'BH'.
`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.

### Value

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.

### Author(s)

Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>

### See Also

`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 DESeq2.

### Examples

        # (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="DESeq2")
        rowData(rseqSE, use.names=TRUE)

[Package EnrichmentBrowser version 2.14.3 Index]