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```
### cst.R (2013-09-01)
###
### Correlation-Shared t-Statistic of Tibshirani-Wassermann
###
### Copyright 2009-2013 Korbinian Strimmer
###
###
### This file is part of the `st' library for R and related languages.
### It is made available under the terms of the GNU General Public
### License, version 3, or at your option, any later version,
### incorporated herein by reference.
###
### This program is distributed in the hope that it will be
### useful, but WITHOUT ANY WARRANTY; without even the implied
### warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
### PURPOSE. See the GNU General Public License for more
### details.
###
### You should have received a copy of the GNU General Public
### License along with this program; if not, write to the Free
### Software Foundation, Inc., 59 Temple Place - Suite 330, Boston,
### MA 02111-1307, USA
cst.stat = function (X, L, verbose=TRUE)
{
FUN = cst.fun(L=L, verbose=verbose)
score = FUN(X)
return( score )
}
cst.fun = function (L, verbose=TRUE)
{
if (missing(L)) stop("Class labels are missing!")
function(X)
{
tmp = centroids(X, L, var.groups=FALSE, centered.data=TRUE,
lambda.var=0, lambda.freqs=0, verbose=verbose)
# differences between the two groups
diff = tmp$means[,1]-tmp$means[,2]
# standard error of diff
n1 = tmp$samples[1]
n2 = tmp$samples[2]
v = tmp$variances[,1] # pooled variance
sd = sqrt( (1/n1 + 1/n2)*v )
# pooled empirical correlation matrix
R = cor(tmp$centered.data)
# t statistic
t = diff/sd
if (verbose) cat("\nComputing correlation-shared t-statistics\n")
# compute correlation-shared t-statistic
p = length(t)
cst.vec = numeric(p)
for (i in 1:p)
{
idx = order(R[i,], decreasing=TRUE) # sort other genes by correlation
nonneg = sum(R[i,] >= 0) # number of nonnegatively associated genes
y = cumsum(abs(t[idx[1:nonneg]]))/1:nonneg # find maximum average
cst.vec[i] = max(y)*sign(t[i])
}
return(cst.vec)
}
}
```

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