SMVar (version 1.3.4)

SMVar.paired: Structural model for variances with paired data

Description

Function to detect differentially expressed genes when data are paired

Usage

SMVar.paired(geneNumbers, logratio, fileexport = NULL, 
            minrep = 2, method = "BH", threshold = 0.05)

Arguments

geneNumbers

Vector with gene names or dataframe which contains all information about spots on the chip

logratio

matrix with one row by gene and one column by replicate giving the logratio

fileexport

file to export the list of differentially expressed genes

minrep

minimum number of replicates to take a gene into account, minrep must be higher than 2

method

method of multiple tests adjustment for p.values

threshold

threshold of False Discovery Rate

Value

Only the number of differentially expressed genes is printed. If asked, the file giving the list of differentially expressed genes is created

If the user creates an object when calling the function (for example "Stat=SMVar.paired(...)") then Stat contains the information for all genes, is sorted by ascending p-values and

Stat$TestStat

gives the test statistics as described in the paper

Stat$StudentPValue

gives the raw p-values

Stat$DegOfFreedom

gives the number of degrees of freedom for the Student distribution for the test statistics

Stat$LogRatio

gives the logratios

Stat$AdjPValue

gives the adjusted p-values

Details

This function implements the structural model for variances described in (Jaffrezic et al., 2007). Data must be normalized before calling the function. Matrix geneNumbers must have one of the following formats: "matrix","data.frame","vector","character","numeric","integer".

References

F. Jaffrezic, Marot, G., Degrelle, S., Hue, I. and Foulley, J. L. (2007) A structural mixed model for variances in differential gene expression studies. Genetical Research (89) 19:25

Examples

# NOT RUN {
library(SMVar)
data(Spleendata)
attach(Spleendata)
SMVar.paired(SpleenGeneId,SpleenLogRatio)
# }