dri.fdrCutoff {DRI}R Documentation

Determine cutoff score for a desired false discovery rate (FDR)

Description

A search is performed for the cutoff score (for either DR-Correlate or DR-SAM) that corresponds to the user-defined FDR.

Usage

dri.fdrCutoff(observed, null, targetFDR, bt = TRUE)

Arguments

observed vector of scores from either drcorrelate or drsam
null matrix of null data from either drcorrelate.null or drsam.null
targetFDR desired false discovery rate
bt either TRUE or FALSE indicating whether a 2-tail test was performed

Details

A binary search is implemented to find the cutoff score that corresponds to the user-defined FDR

Value

comp1 a two element list containing the number of genes found significant at the chosen FDR, and the score cutoff corresponding to that FDR.

Author(s)

Keyan Salari, Robert Tibshirani, and Jonathan R. Pollack

References

Salari, K., Tibshirani, R., and Pollack, J.R. (2009) DR-Integrator: a new analytic tool for integrating DNA copy number and gene expression data. http://pollacklab.stanford.edu/

See Also

drcorrelate, drcorrelate.null, drsam, drsam.null, dri.fdrCutoff, dri.sig_genes, dri.heatmap, dri.merge.CNbyRNA, dri.smooth.cghdata, runFusedLasso

Examples

require(impute)
data(mySampleData)
attach(mySampleData)

# DNA data should contain no missing values - pre-smooth beforehand
# Impute missing values for gene expression data
RNA.data <- dri.impute(RNA.data)

# DR-Correlate analysis to find genes with correlated DNA/RNA measurements
obs <- drcorrelate(DNA.data, RNA.data, method="pearson")
# generate null distribution for FDR calculation (10 permutations)
null <- drcorrelate.null(DNA.data, RNA.data, method="pearson", perm=10)
# identify the correlation cutoff corresponding to your desired FDR
n.cutoff <- dri.fdrCutoff(obs, null, targetFDR=0.05, bt=TRUE)
cutoff <- n.cutoff[2]
# retrieve all genes that are significant at the determined cutoff, and
# calculate gene-specific FDRs
Results <- dri.sig_genes(cutoff, obs, null, GeneIDs, GeneNames, Chr, Nuc, 
bt=TRUE, method="drcorrelate") 

# DR-SAM analysis to find genes with alterations in both DNA and RNA between
# different classes
labels <- c(rep(1,25), rep(2,25)) # 25 samples in class 1 and 25 in class 2
obs <- drsam(DNA.data, RNA.data, labels, transform.type="raw")
# generate null distribution for FDR calculation (10 permutations)
null <- drsam.null(DNA.data, RNA.data, labels, transform.type="raw", 10)
# identify the correlation cutoff corresponding to your desired FDR
n.cutoff <- dri.fdrCutoff(obs$test.summed, null, targetFDR=0.05, bt=TRUE)
cutoff <- n.cutoff[2]
# retrieve all genes that are significant at the determined cutoff, and
# calculate gene-specific FDRs
Results <- dri.sig_genes(cutoff, obs, null, GeneIDs, GeneNames, Chr, Nuc, 
bt=TRUE, method="drsam") 

[Package DRI version 1.1 Index]