fdr.adjust: FDR adjustment procedures

In LPE: Methods for analyzing microarray data using Local Pooled Error (LPE) method

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. Can be "resamp", "BH", "BY", "Bonferroni" or "mix.all"
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. BH and BY give Benjamini-Hochberg and Benjamini-Yekutieli adjusted FDRs (adopted from multtest procedure), Bonferroni adjusted p-values and "mix.all" gives SAM-like FDR adjustment. For further details on the comparisons of each of these methods, please see the reference paper (Rank-invariant resampling...) mentioned below. Users are encouraged to use FDR instead of Bonferrni adjsusted p-value as initial cutoffs while selecting the significant genes. Bonferroni adjusted p-values are provided under Bonferroni method here just for the sake of completion for the users who want it.

Author(s)

Nitin [e-mail]

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.

Jain et. al. (2005) Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data, BMC Bioinformatics, Vol 6, 187.

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)
 final.result
  

LPE documentation built on Nov. 8, 2020, 5:25 p.m.