11RNAseq: Topic: Analysis of RNA-seq Data
In hdeberg/limma: Linear Models for Microarray Data

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.

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 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

hdeberg/limma documentation built on Dec. 20, 2021, 3:43 p.m.