# Plot FDRs for both T and LPC Takes the output of a call to EstimateLPCFDR, and uses it to plot the false discovery rates of the genes with highest LPC / T scores. ```r PlotFDRs(lpcfdr.out, frac=.25) ``` ## Arguments - `lpcfdr.out`: Output of a call to EstimateLPCFDR. - `frac`: The fraction of genes (with highest T/LPC scores) for which the T/LPC FDRs are plotted. Default is .25 (25%). ## Returns Nothing is returned. ## References Witten, D.M. and Tibshirani, R. (2008) Testing significance of features by lassoed principal components. Annals of Applied Statistics. http://www-stat.stanford.edu/~dwitten ## Author(s) Daniela M. Witten and Robert Tibshirani ## Examples ```r ###### not running due to timing; uncomment to run #### #set.seed(2) #n <- 40 # 40 samples #p <- 1000 # 1000 genes #x <- matrix(rnorm(n*p), nrow=p) # make 40x1000 gene expression matrix #y <- rnorm(n) # quantitative outcome ## make first 50 genes differentially-expressed #x[1:25,y<(-.5)] <- x[1:25,y<(-.5)]+ 1.5 #x[26:50,y<0] <- x[26:50,y<0] - 1.5 ## compute LPC and T scores for each gene #lpc.obj <- LPC(x,y, type="regression") ## Look at plot of Predictive Advantage #pred.adv <- #PredictiveAdvantage(x,y,type="regression",soft.thresh=lpc.obj$soft.thresh) ## Estimate FDRs for LPC and T scores #fdr.lpc.out <- #EstimateLPCFDR(x,y,type="regression",soft.thresh=lpc.obj$soft.thresh,nreps=50) ## Estimate FDRs for T scores only. This is quicker than computing FDRs ## for LPC scores, and should be used when only T FDRs are needed. If we ## started with the same random seed, then EstimateTFDR and EstimateLPCFDR ## would give same T FDRs. #fdr.t.out <- EstimateTFDR(x,y, type="regression") ## print out results of main function #lpc.obj ## print out info about T FDRs #fdr.t.out ## print out info about LPC FDRs #fdr.lpc.out ## Compare FDRs for T and LPC on 6% of genes. In this example, LPC has ## lower FDR. #PlotFDRs(fdr.lpc.out,frac=.06) ## Print out names of 20 genes with highest LPC scores, along with their ## LPC and T scores. #PrintGeneList(lpc.obj,numGenes=20) ## Print out names of 20 genes with highest LPC scores, along with their ## LPC and T scores and their FDRs for LPC and T. #PrintGeneList(lpc.obj,numGenes=20,lpcfdr.out=fdr.lpc.out) ## Now, repeating everything that we did before, but using a ## **survival** outcome ## Not run due to timing #set.seed(2) #n <- 40 # 40 samples #p <- 1000 # 1000 genes #x <- matrix(rnorm(n*p), nrow=p) # make 40x1000 gene expression matrix #y <- rnorm(n) + 10 # survival times; must be positive ## censoring outcome: 0 or 1 #cens <- rep(1,40) # Assume all observations are complete ## make first 50 genes differentially-expressed #x[1:25,y<9.5] <- x[1:25,y<9.5]+ 1.5 #x[26:50,y<10] <- x[26:50,y<10] - 1.5 ##lpc.obj <- LPC(x,y, type="survival", censoring.status=cens) ## Look at plot of Predictive Advantage #pred.adv <- PredictiveAdvantage(x,y,type="survival",soft.thresh=lpc.obj$soft.thresh, #censoring.status=cens) ## Estimate FDRs for LPC scores and T scores #fdr.lpc.out <- EstimateLPCFDR(x,y, type="survival", #soft.thresh=lpc.obj$soft.thresh,nreps=20,censoring.status=cens) ## Estimate FDRs for T scores only. This is quicker than computing FDRs ## for LPC scores, and should be used when only T FDRs are needed. If we ## started with the same random seed, then EstimateTFDR and EstimateLPCFDR ## would give same T FDRs. #fdr.t.out <- EstimateTFDR(x,y, type="survival", censoring.status=cens) ## print out results of main function #lpc.obj ## print out info about T FDRs #fdr.t.out ## print out info about LPC FDRs #fdr.lpc.out ## Compare FDRs for T and LPC scores on 10% of genes. #PlotFDRs(fdr.lpc.out,frac=.1) ## Print out names of 20 genes with highest LPC scores, along with their ## LPC and T scores. #PrintGeneList(lpc.obj,numGenes=20) ## Print out names of 20 genes with highest LPC scores, along with their ## LPC and T scores and their FDRs for LPC and T. #PrintGeneList(lpc.obj,numGenes=20,lpcfdr.out=fdr.lpc.out) ```

PlotFDRs function