DRI-package {DRI}R Documentation

DR-Integrator: an analytic tool for integrating DNA copy number and gene expression data

Description

DR-Integrator identifies genes with significant correlations between DNA copy number alterations and gene expression data, and implements a supervised learning analysis that captures genes with significant alterations in both DNA copy number and gene expression between two sample classes.

Details

Package: DRI
Type: Package
Version: 1.1
Date: 2009-11-16
License: GPL-2

This package contains two analytic tools: DR-Correlate and DR-SAM.

Author(s)

Keyan Salari, Robert Tibshirani, Jonathan R. Pollack

Maintainer: Keyan Salari <ksalari@stanford.edu>

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") 

# Optional heatmap plot for significant DR-Correlation genes
sample.names <- colnames(DNA.data)
pdf(file="DRI-Heatmap.pdf", height=8, width=11)
dri.heatmap(Results, DNA.data, RNA.data, sample.names, GeneNames, Chr, Nuc, 
statistic="pearson", color.scheme="RG")
dev.off()

# 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]