| EstimateLPCFDR {lpc} | R Documentation |
Estimate LPC False Discovery Rates.
Description
An estimated false discovery rate for each gene is computed, based on the LPC scores. Estimated false discovery rates based on T scores (Cox, regression coefficient, or two-sample t-statistic) are also given.
Usage
EstimateLPCFDR(x, y, type, nreps=100,soft.thresh=NULL, censoring.status=NULL)
Arguments
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 multiclass, y is coded as 1,2,3,... |
type |
One of "regression" (for a quantitative outcome), "two class", "multiclass", or "survival". |
nreps |
The number of training/test set splits used to estimate LPC's false discovery rates. Default is 100. |
soft.thresh |
The value of the soft threshold to be used in the L1 regression of the scores onto the eigenarrays. This can be the value of the soft-threshold chosen adaptively by LPC function when it is run on the data; if not entered by the user, then that adaptive value is computed. |
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. |
Details
Details of false discovery rate estimation for LPC can be found in the paper: http://www-stat.stanford.edu/~dwitten \
As explained in the paper, FDR of LPC is estimated by computing the FDR of simpler scores (Cox for survival outcome, standardized regression coefficients for regression outcome, and two-sample t-statistic for two-class outcome) and then estimating the difference between the FDR of LPC and the FDR of these simpler scores.
Value
fdrlpc |
A vector of length p (equal to the number of genes), with the LPC false discovery rate for each gene. |
fdrt |
A vector of length p (equal to the number of genes), with the T false discovery rate for each gene. |
fdrdiff |
The FDR of T minus the FDR of LPC. This is approximately equal to fdrt-fdrlpc, with the caveat that the FDR of LPC (computed via this difference) must be between 0 and 1. |
pi0 |
The fraction of genes that are believed to be null. |
soft.thresh |
The value of the soft threshold used in the L1 constraint for LPC. |
call |
The function call made. |
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
### 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 TIME CONSTRAINTS -- 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)