1d:T770,# This package implements the Lassoed Principal Components (LPC) method. It is used to compute LPC scores for each gene in a microrray experiment with a survival, quantitative, or two-class outcome. LPC scores are computed for each gene; the method borrows strength across genes and can result in more accurate gene scores than simpler statistics. In this package, the LPC method is applied by regressing Cox scores (survival outcome), two-sample t-statistics (two-class outcome), or standardized regression coefficients (quantitative outcome) onto gene expression eigenarrays, with an L1 constraint. package ## Details ||| |:--|:--| |Package:|lpc| |Type:|Package| |Version:|1.0.2| |Date:|2013-12-15| |License:|GPL (>= 2)| The main function is "LPC", which computes LPC scores for each gene. The matrix of gene expression data, a vector of outcome, and the outcome type must be passed in to this function. ## Author(s) Daniela M. Witten and Robert Tibshirani Maintainer: Daniela M. Witten ## References Witten, DM and Tibshirani R (2008) Testing significance of features by lassoed principal components. Annals of Applied Statistics. ## See Also www.biostat.washington.edu/~dwitten ## Examples ```r ### Uncomment to run #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") #lpc.obj ## 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) #fdr.lpc.out ```

lpc-package