voomlimmaFit: Analysis of RFI RNA-seq data Using voom

In rmRNAseq: RNA-Seq Analysis for Repeated-Measures Data

Description

This function analyzes RFI RNA-seq data and simulated datasets using voom, which uses precision weights and linear model pipeline for the analysis of log-transformed RNA-seq data.

Usage

voomlimmaFit(counts, design, Effect)

Arguments

counts	a matrix of count data.
design	a design matrix.
Effect	the effect used to simulate data, either line2, or time. This effect is considered as the main factor of interest where the status of DE and EE genes was specified.

Value

a list of 4 components

fit	output of voom-limma fit.
pv	a vector of p-values of the test for significant of Effect.
qv	a vector of q-values corresponding to the pv above.

References

1. Gordon K. Smyth, Matthew Ritchie, Natalie Thorne,James Wettenhall, Wei Shi and Yifang Hu. limma: Linear Models for Microarray and RNA-Seq Data. User's Guide. https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf

2. Gordon K. Smyth. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3. Epub 2004 Feb 12.

Examples

data(dat)
data(design)
counts <- dat[1:50,]
design <- design
Effect <- "line2"
voomlimmaout <- rmRNAseq:::voomlimmaFit(counts, design, Effect)
names(voomlimmaout)

rmRNAseq documentation built on Nov. 8, 2019, 5:06 p.m.