lpc (version 1.0.2.1)

PrintGeneList: Print the list of genes with highest LPC scores

Description

Takes the output of a call to LPC function, and prints out the (T and LPC) scores and names of the top-scoring genes. It can (optionally) also print out FDRs for the T and LPC scores.

Usage

PrintGeneList(lpc.obj, numGenes=100,gene.names=NULL, lpcfdr.out=NULL)

Arguments

lpc.obj

Output of a call to LPC.

numGenes

Desired length of list of top genes.

gene.names

Vector containing gene names; should have length equal to the number of genes in the data set.

lpcfdr.out

Optional paramater, it is the output of the function EstimateLPCFDR. If this is passed in, then FDRs for LPC and T will also be printed.

Value

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

Examples

# NOT RUN {
# Not running 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) # 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

#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)


# }