This package offers functions to perform regularized Hotelling's T-square test for pathway or gene set analysis. The package is tailored for but not limited to proteomics data, in which sample sizes are often small, and a large proportion of the data are missing and/or correlations may be present.
Lin S. Chen and Pei Wang
Maintainer: Lin S. Chen <email@example.com>
Chen LS, Paul D, Prentice RL and Wang P. (2011) A regularized Hotelling's T-square test for pathway analysis in proteomics studies. Journal of the American Statistical Association, in press.
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## we simulate a data set with N=10 samples and p=50 proteins. ## 20% of the data are missing. ## Among the 50 proteins, we randomly assign 2 pathways, with 5 and 12 proteins, respectively. set.seed(1) X <- matrix(rnorm(500),nrow=10) X[sample(1:500, 0.2*500)] <- NA path.idx <- list() path.idx[] <- 1:5 path.idx[] <- 13:24 names(path.idx) <- c("pathway A", "pathway B") ## The following function tests each pathway to see ## if any of the proteins in each pathway shows non-zero ## abundance/expression pval <- RHT.fun(path.idx, dat=X)
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