limma (version 3.28.14)

11.RNAseq: 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.

Arguments

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