PlotFDRs {lpc}

Plot FDRs for both T and LPC

Description

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.

Usage

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%).

Value

Nothing is returned.

Author(s)

Daniela M. Witten and Robert Tibshirani

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

Examples

###### 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)