EstimateTFDR: Estimate T False Discovery Rates.
In lpc: Lassoed Principal Components for Testing Significance of Features

Description Usage Arguments Details Value Author(s) References Examples

An estimated false discovery rate for each gene is computed, based on the T scores. The T scores are as follows, for two-class, survival, and quantitative outcomes: two-sample t-statistics, Cox scores, standardized regression coefficients. The output of this function is identical to the outputs "fdrt" and "pi0" of the function EstimateLPCFDR. This function should be used if only the FDR of T is desired, because computing the FDR of LPC is time-consuming.

1	EstimateTFDR(x,y, type,censoring.status=NULL)

`x`	The matrix of gene expression values; pxn where n is the number of observations and p is the number of genes.
`y`	A vector of length n, with an outcome for each observation. For two-class outcome, y's elements are 1 or 2. For quantitative outcome, y's elements are real-valued. For survival data, y indicates the survival time. For a multiclass outcome, y is coded as 1,2,3,...
`type`	One of "regression" (for a quantitative outcome), "two class", "survival", or "multiclass" (for a multiple-class outcome).
`censoring.status`	For survival outcome only, a vector of length n which takes on values 0 or 1 depending on whether the observation is complete or censored.

False discovery rates are estimated by permutations, as in e.g. Tusher et al (2001) and Storey & Tibshirani (2003).

`fdrt`	A vector of length p (equal to the number of genes), with the T false discovery rate for each gene. Note that this is identical to the "fdrt" output by the function EstimateLPCFDR.
`pi0`	The fraction of genes that are believed to be null.
`call`	The function call made.

Daniela M. Witten and Robert Tibshirani

Storey, J.D. and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences. 100(16): 9440-9445.

Tusher, V.G. and Tibshirani, R. and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences. 98(9): 5116-5121.

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 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) + 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)