resamp.adj {LPE}R Documentation

Resampling based fdr adjustment

Description

Adjusts the fdr based on rank invariant genes

Usage

 resamp.adj(x,y, q=0.01, iterations=5, min.genes.int=10) 

Arguments

x Replicated data from first experimental condition (as matrix or data-frame)
y Replicated data from second experimental condition (as matrix or data-frame)
q q is the quantile width; q=0.01 corresponds to 100 quantiles
iterations Number of iterations to be performed to obtain critical z-statistics
min.genes.int Determines the minimum number of genes in a subinterval for selecting the adaptive intervals.

Details

Returns the z-statistics for the null distribution, obtained from resampling the rank invariant genes within each quantile. These z-statistic values are compared with z-statiscs from the original data, and fdr is calculated.

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.

 # Subsetting the data
 subset.Ley <- Ley[1:1000,]
  
  subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
   
 # 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])
  

 
 z.stats.null <- resamp.adj(subset.Ley[,2:4], subset.Ley[,5:7], q=0.01, iterations=2,min.genes.int=10 )


[Package LPE version 1.1.5 Index]