shrinkt.stat {st} | R Documentation |
shrinkt.stat
and shrinkt.fun
compute the ``shrinkage t'' statistic
of Opgen-Rhein and Strimmer (2007).
shrinkt.stat(X, L, var.equal=TRUE, verbose=TRUE) shrinkt.fun(L, var.equal=TRUE, verbose=TRUE)
X |
data matrix. Note that the columns correspond to variables (``genes'') and the rows to samples. |
L |
factor containing class labels for the two groups. |
var.equal |
assume equal (default) or unequal variances in each group. |
verbose |
print out some (more or less useful) information during computation. |
The ``shrinkage t'' statistic is similar to the usual t statistic, with the replacement of the sample variances by corresponding shrinkage estimates. These are derived in a distribution-free fashion and with little a priori assumptions. Using the ``shrinkage t'' statistic procduces highly accurate rankings - see Opgen-Rhein and Strimmer (2007).
The``shrinkage t'' statistic can be generalized to include gene-wise correlation,
see shrinkcat.stat
.
shrinkt.stat
returns a vector containing the ``shrinkage t''
statistic for each variable/gene.
The corresponding shrinkt.fun
functions return a function that
produces the ``shrinkage t'' statistics when applied to a data matrix
(this is very useful for simulations).
Rainer Opgen-Rhein and Korbinian Strimmer (http://strimmerlab.org).
Opgen-Rhein, R., and K. Strimmer. 2007. Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Statist. Appl. Genet. Mol. Biol. 6:9. (http://www.bepress.com/sagmb/vol6/iss1/art9/)
studentt.stat
,
diffmean.stat
,
shrinkcat.stat
.
# load st library library("st") # load Choe et al. (2005) data data(choedata) X <- choe2.mat dim(X) # 6 11475 L <- choe2.L L # L may also contain some real labels L = c("group 1", "group 1", "group 1", "group 2", "group 2", "group 2") # shrinkage t statistic (equal variances) score = shrinkt.stat(X, L) order(abs(score), decreasing=TRUE)[1:10] # [1] 10979 11068 50 1022 724 5762 43 4790 10936 9939 # lambda.var (variance vector): 0.3882 # shrinkage t statistic (unequal variances) score = shrinkt.stat(X, L, var.equal=FALSE) order(abs(score), decreasing=TRUE)[1:10] # [1] 11068 50 10979 724 43 1022 5762 10936 9939 9769 # lambda.var (variance vector): 0.3673 0.3362 # compute q-values and local false discovery rates library("fdrtool") fdr.out = fdrtool(score) sum( fdr.out$qval < 0.05 ) sum( fdr.out$lfdr < 0.2 ) fdr.out$param