drcorrelate.null {DRI} | R Documentation |
A null distribution is generated by randomly permuting the sample labels of the gene expression data matrix and recomputing the DNA/RNA correlations for each gene.
drcorrelate.null(DNA, RNA, method = "pearson", tail_p = 10, perm)
DNA |
matrix of DNA copy number data |
RNA |
matrix of gene expression data, samples (columns) in same order as DNA matrix |
method |
correlation statistic, either "pearson", "spearman", or "ttest" |
tail_p |
top/bottom percent of samples (with respect to the gene's copy number) to use for extremes t-test groups; used only when method = "ttest" |
perm |
number of permutations to perform |
null |
n * k matrix of null data, where n = number of genes and k = number of permutations |
Keyan Salari, Robert Tibshirani, and 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-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")