limmaAnalyze: use limma to analyze the PDUI

In InPAS: InPAS: a bioconductor package for the identification of novel alternative PolyAdenylation Sites (PAS) using RNA-seq data

Description

use limma to analyze the PDUI

Usage

limmaAnalyze(UTR3eset, design, contrast.matrix, coef=1, robust=FALSE, ...)

Arguments

UTR3eset	an UTR3eSet object
design	the design matrix of the experiment, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates. see model.matrix
contrast.matrix	numeric matrix with rows corresponding to coefficients in fit and columns containing contrasts. May be a vector if there is only one contrast. see makeContrasts
coef	column number or column name specifying which coefficient or contrast of the linear model is of interest. see more topTable. default value: 1
robust	logical, should the estimation of the empirical Bayes prior parameters be robustified against outlier sample variances?
...	other arguments are passed to lmFit.

Value

fit results of eBayes by limma. It is an object of class MArrayLM containing everything found in fit. see eBayes

Author(s)

Jianhong Ou

See Also

singleSampleAnalyze,singleGroupAnalyze, fisher.exact.test

Examples

    library(limma)
    path <- file.path(find.package("InPAS"), "extdata")
    load(file.path(path, "eset.MAQC.rda"))
    tags <- colnames(eset$PDUI.log2)
    g <- factor(gsub("\\..*$", "", tags))
    design <- model.matrix(~-1+g)
    colnames(design) <- c("Brain", "UHR")
    contrast.matrix <- makeContrasts(contrasts="Brain-UHR",levels=design)
    res <- limmaAnalyze(eset, design, contrast.matrix)
    head(res)

InPAS documentation built on Nov. 8, 2020, 5:03 p.m.