## Not run:
# ### Leukemia data set of Golub et al. (1999).
# library(golubEsets)
# data(Golub_Train)
#
# ### Variance-stabilizing normalization of Huber et al. (2002).
# library(vsn)
# golubNorm <- justvsn(Golub_Train)
#
# ### A binary vector of class labels.
# id <- as.numeric(Golub_Train$ALL.AML)
#
# ### Do an unpaired t-test.
# ### Let's have a quick example with 50 filtered permutations only.
# ### With num.take=10, we only need 5 iteration steps.
# yperm <- twilight.filtering(golubNorm,id,method="t",num.perm=50,num.take=10)
# dim(yperm)
#
# ### Let's check that the filtered permutations really produce uniform p-value distributions.
# ### The first row is the original labeling, so we try the second permutation.
# yperm <- yperm[-1,]
# b <- twilight.pval(golubNorm,yperm[1,],method="t",yperm=yperm)
# hist(b$result$pvalue)
# ## End(Not run)
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