fdr.adjust {LPE}R Documentation

FDR adjustment procedures

Description

Based on the type of adjustment, eg: resampling, BH, BY, etc, calls appropriate functions for fdr adjustment

Usage

 fdr.adjust(lpe.result,adjp="resamp",target.fdr=c(10^-3 ,seq(0.01,0.10,0.01), 0.15, 0.20, 0.50),iterations=5,ALL=FALSE )

Arguments

lpe.result Data frame obtained from calling lpe function
adjp Type of adjustment procedure
target.fdr Desired FDR level (used only for resampling based adjustment)
iterations Number of iterations for stable z-critical.
ALL If TRUE, the FDR corresponding to all the z-statistics, i.e. for every gene intensity is given.

Details

Returns the output similar to lpe function, including adjusted fdr

References

J.K. Lee and M.O.Connell(2003). An S-Plus library for the analysis of differential expression. In The Analysis of Gene Expression Data: Methods and Software. Edited by G. Parmigiani, ES Garrett, RA Irizarry ad SL Zegar. Springer, NewYork.

Jain et. al. (2003) Local pooled error test for identifying differentially expressed genes with a small number of replicated microarrays, Bioinformatics, 1945-1951.

Examples


 # Loading the library and the data
 library(LPE)
 data(Ley)
 
 dim(Ley)
 # Gives 12488*7 
 # First column is ID.

 Ley[,2:7] <- preprocess(Ley[,2:7],data.type="MAS5")

 # Subsetting the data
 subset.Ley <- Ley[1:1000,]
  
   
 # Finding the baseline distribution of condition 1 and 2.
 var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
 var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01)

 # Applying LPE
 lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2,
                probe.set.name=subset.Ley[,1])

 final.result <- fdr.adjust(lpe.result, adjp="resamp", target.fdr=c(0.01,0.05), iterations=1)


[Package LPE version 1.1.5 Index]