BuildLimmaLM {DiPALM}

Fit linear models to time-course expression patterns using the limma package

Description

This function fits a linear model (using lmFit form the limma package) to each set of kME values returned from BuildModMembership.

Usage

BuildLimmaLM(dataMat, designMat, contrastStr)

Arguments

dataMat

a matrix containing module membership values (a single element of the list returned by BuildModMembership). Module membership is a Pearson correlation value for a single gene compared to a module eigengene.

designMat

a linear model design matrix containing binary values (0 and 1) with rows as samples and columns as treatments. Use the model.matrix function in the stats package.

contrastStr

a linear model contrast specifying the desired differential comparison to be made using the linear model (see the limma package documentation for more details).

Details

This function uses the limma package functionality to carry out an analysis of differential patterning. Asking if a gene displays a significantly different expression pattern across time when looking at one condition compared to another. The concept of differential patterning is similar to differential expression. However, the expression level of most genes fluctuates significantly throughout the day. Most of this is due to diurnal or circadian rhythms. These regular changes in expression severely complicate differential expression analysis and give rise to the need for examining differential patterning. By "encoding" the expression pattern of each gene into a vector of module membership (kME) values using the BuildModMembership function, the DiPALM analysis provides a way to use the limma method to examine differential patterning.

Value

This function returns an MArrayLM-class object. This is a custom class from the limma package. It is an object that contains all information and results of individual linear models built for each gene in the input matrix.

Author(s)

Ryan C. Sartor

See Also

BuildLimmaLM , lmFit , makeContrasts , contrasts.fit , eBayes

Examples

data("testData")
# First calculate the module memberships
kMEsList<-BuildModMembership(MeMat=testData$moduleEigengenes, TCsLst=testData$timeCourseList)

# Then build the limma models
LimmaMods<-lapply(kMEsList, function(x) 
BuildLimmaLM(dataMat = x, designMat = testData$modelDesign, contrastStr = testData$modelContrast))