11.RNAseq {limma} | R Documentation

## Topic: Analysis of RNA-seq Data

### Description

This page gives an overview of LIMMA functions to analyze RNA-seq data.

`voom`

  Transform RNA-seq or ChIP-seq counts to log counts per million (log-cpm) with associated precision weights. After this tranformation, RNA-seq or ChIP-seq data can be analyzed using the same functions as would be used for microarray data.

`voomWithQualityWeights`

  Combines the functionality of `voom` and `arrayWeights`.

`diffSplice`

  Test for differential exon usage between experimental conditions.

`topSplice`

  Show a data.frame of top results from `diffSplice`.

`plotSplice`

  Plot results from `diffSplice`.

`plotExons`

  Plot logFC for individual exons for a given gene.

### References

Law, CW, Chen, Y, Shi, W, Smyth, GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29. http://genomebiology.com/2014/15/2/R29

Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47. http://nar.oxfordjournals.org/content/43/7/e47

### See Also

See also the edgeR package for normalization and data summaries of RNA-seq data, as well as for alternative differential expression methods based on the negative binomial distribution. `voom` accepts DGEList objects and normalization factors from edgeR.

01.Introduction, 02.Classes, 03.ReadingData, 04.Background, 05.Normalization, 06.LinearModels, 07.SingleChannel, 08.Tests, 09.Diagnostics, 10.GeneSetTests, 11.RNAseq

[Package limma version 3.42.2 Index]