PlotFDRs: Plot FDRs for both T and LPC
In lpc: Lassoed Principal Components for Testing Significance of Features

Description Usage Arguments Value Author(s) References Examples

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.

1	PlotFDRs(lpcfdr.out, frac=.25)

`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%).

Nothing is returned.

Daniela M. Witten and Robert Tibshirani

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

###### 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)