dri.sig_genes {DRI} | R Documentation |
Given a cutoff score corresponding to the desired FDR, the list of significant genes is generated, along with each gene's FDR.
dri.sig_genes(cutoff, observed, null_dist, gene_id, gene_name, chr, nuc, bt = TRUE, method = "drcorrelate")
cutoff |
cutoff score for significance from dri.fdrCutoff |
observed |
vector of observed scores from drcorrelate or drsam |
null_dist |
matrix of null data from drcorrelate.null or drsam.null |
gene_id |
vector of gene IDs |
gene_name |
vector of gene names |
chr |
vector of gene chromosome locations |
nuc |
vector of gene nucleotide positions |
bt |
either TRUE or FALSE indicated whether a 2-tail test was performed |
method |
analysis method used, either "drcorrelate" or "drsam" |
Results.SigGenes |
a list of significant genes, positive and negative, with gene-specific FDRs |
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
require(impute) data(mySampleData) attach(mySampleData) # DNA data should contain no missing values - pre-mooth 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")