deAna: Differential expression analysis between two sample groups

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/deAna.R

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

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

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.

filter.by.expr

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 filterByExpr function from the edgeR package. Defaults to TRUE.

assay

Character. The name of the assay for differential expression analysis if expr is a SummarizedExperiment with *multiple assays*. Defaults to "auto", which automatically determines the appropriate assay based on data type provided and DE method selected. See details.

Details

Using a SummarizedExperiment with *multiple assays*:

For the typical use case within the EnrichmentBrowser workflow this will be a 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 normalize function.

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.

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, glmQLFit for DE analysis with edgeR, and DESeq for DE analysis with DESeq2.

Examples

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    # (1) microarray data: intensity measurements
    maSE <- makeExampleData(what = "SE", type = "ma")
    maSE <- deAna(maSE)
    rowData(maSE)
    
    # (2) RNA-seq data: read counts
    rseqSE <- makeExampleData(what = "SE", type = "rseq")
    rseqSE <- deAna(rseqSE, de.method = "DESeq2")
    rowData(rseqSE)

lgeistlinger/EnrichmentBrowser documentation built on July 26, 2021, 9:54 p.m.