DRI-package {DRI} | R Documentation |
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.
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.
Keyan Salari, Robert Tibshirani, Jonathan R. Pollack
Maintainer: Keyan Salari <ksalari@stanford.edu>
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/
drcorrelate
, drcorrelate.null
, drsam
,
drsam.null
, dri.fdrCutoff
, dri.sig_genes
,
dri.heatmap
, dri.merge.CNbyRNA
, dri.smooth.cghdata
,
runFusedLasso
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")