AffyExpress (version 1.38.0)

regress: Run regression to fit genewise linear model

Description

Fit genewise linear model using LIMMA package, ordinary linear regression, or permutation method.

Usage

regress(object, design, contrast, method, adj="none", permute.time=1000)

Arguments

object

an "ExpressionSet"

design

design matrix from the make.design function

contrast

contrast matrix from the make.contrast function

method

Three methods are supported by this function: "L" for using LIMMA method - compute moderated t-statistics and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value, "F" for using ordinary linear regression, "P" for permuation test by resampling the phenotype

adj

adjustment method for multiple comparison test, including "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". The default value is "none". Type help(p.adjust) for more detail.

permute.time

number of permutation times, only used for the permutation method.

Value

A dataframe contains rows for all the genes from object and the following columns: ID(probeid); Log2Ratio (estimate of the effect or the contrast, on the log2 scale); F (F statistics); P.Value (raw p-value); adj.P.Value (adjusted p-value or q-value)

References

Smyth, G.K. (2005) Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420

Examples

data(testData)
normaldata<-pre.process("rma",testData)

## Create design matrix
design<-make.design(pData(normaldata), "group")

## Create contrast matrix - Compare group "A" vs. "C"
contrast<-make.contrast(design, "A", "C")

## Identify differentially expressed gene by using LIMMA method
result<-regress(normaldata, design, contrast, "L")