drsam {DRI} | R Documentation |
A test is performed to identify genes with significant differences in both DNA copy number and gene expression between two sample groups of interest.
drsam(DNA.data, RNA.data, labels, transform.type, for.null = FALSE)
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 |
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.
observed |
vector of observed DR-SAM scores for each gene |
Keyan Salari, Robert Tibshirani, Jonathan R. Pollack
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-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")