drsam {DRI}R Documentation

Perform supervised learning analysis between 2 sample classes for DNA/RNA differences

Description

A test is performed to identify genes with significant differences in both DNA copy number and gene expression between two sample groups of interest.

Usage

drsam(DNA.data, RNA.data, labels, transform.type, for.null = FALSE)

Arguments

DNA.data matrix of DNA copy number data
RNA.data matrix of gene expression data, samples (columns) in same order as DNA matrix
labels class labels of the two comparison groups, either 1 or 2
transform.type type of transformation to apply to data, either "standardize", "rank", or "raw"
for.null used internally by drsam.null, keep as default

Details

DR-SAM (DNA/RNA-Significance Analysis of Microarrays) performs a supervised analysis to identify genes with statistically significant differences in both DNA copy number and gene expression between different classes (e.g., tumor subtype-A vs. tumor subtype-B). The goal of this analysis is to identify genetic differences (CNAs) that mediate gene expression differences between two groups of interest. DR-SAM implements a modified Student's t-test to generate for each gene two t-scores assessing differences in DNA copy number and differences in gene expression. A final score is computed by first summing the copy number t-score and gene expression t-score, and then weighting the sum by the ratio of the two t-scores. The weight is applied to favor genes with strong differences in both DNA copy number and gene expression between the two classes.

Value

observed vector of observed DR-SAM scores for each gene

Author(s)

Keyan Salari, Robert Tibshirani, 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-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]