data matrix. Note that the columns correspond to variables (“genes”)
and the rows to samples.
L
vector with class labels for the two groups.
verbose
print out some (more or less useful) information during computation.
Details
The correlation-shared t-statistic for a gene is computed as
the average of t-scores correlated with that gene. For mathematical
details see Tibshirani and Wasserman (2006).
Value
cst.stat returns a vector containing correlation-shared t-statistic for each variable/gene.
The corresponding cst.fun functions return a function that
computes the correlation-shared t-statistic when applied to a data matrix
(this is very useful for simulations).
Tibshirani, R., and L. Wasserman. 2006. Correlation-sharing for detection of differential gene expression.
See http://arxiv.org/abs/math/0608061 for publication details.
See Also
shrinkcat.stat, lait.stat.
Examples
# load st library
library("st")
# prostate data set
data(singh2002)
X = singh2002$x
L = singh2002$y
dim(X) # 102 6033
length(L) # 102
# correlation shared t statistic
## Not run:
score = cst.stat(X, L)
idx = order(abs(score), decreasing=TRUE)
idx[1:10]
# [1] 610 1720 364 332 914 3940 4546 1068 579 4331
## End(Not run)
# compared with:
# Student t statistic
score = studentt.stat(X, L)
idx = order(abs(score), decreasing=TRUE)
idx[1:10]
# [1] 610 1720 364 332 914 3940 4546 1068 579 4331
# for the same example using the shrinkage cat score see shrinkcat.stat()