[R] Wilcoxon test on data matrix

[Booman, M]

Dear everyone,
I am trying to do a Wilcoxon one-sided test on my gene expression data.
These are the data i have in R:
data.matrix (matrix, numeric) containing all gene expression data (42 rows=genes,  42 columns=tumors), no column header or row names
data.cl (vector, numeric) consisting of 42 0's and 1's to indicate class 0 or class 1 for each column in data.matrix

I want to do a Wilcoxon one-sided test on the data from class 0 versus the data from class 1, for each row (gene) of the data set.
My first try:

#to make separate matrices for both classes:
data.matrix.0 <- data.matrix[,data.cl==0] 
data.matrix.1 <- data.matrix[,data.cl==1]

# to run the wilcox.test function for each row:
rawp <- apply(data.matrix.0, 1, wilcox.test, y=data.matrix.1, alternative="less")


The result of printing rawp is:
$`1`

	Wilcoxon rank sum test with continuity correction

data:  newX[, i] and data.matrix.1 
W = 7585, p-value = 1
alternative hypothesis: true location shift is less than 0 


$`2`

	Wilcoxon rank sum test with continuity correction

data:  newX[, i] and data.matrix.1 
W = 6700, p-value = 0.9983
alternative hypothesis: true location shift is less than 0 


Etcetera for each row of the data matrix. 
I can get the p value for one row (gene) using:
rawp.1 <- rawp$'1'$p.value

But how can I get these p-values in one list? I have tried:
rawp <- NULL
for (i in 1:42) {
	a <- paste("'", i, "'", sep="")
	rawp <- rbind(rawp, test$a$p.value)
 }

but that doesn't work (no errors but rawp stays NULL)

There must be an easier way to do a wilcoxon analysis on a matrix!
I'd appreciate your help with this...

Marije


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