Using t.test() in this case will likely be very slow. A faster
alternative would be to use rowttests() from the genefilter package of
Bioconductor.
Best,
Jim
Henrique Dallazuanna wrote:
> You can try this:
>
> cbind(data.sub, p.value=apply(data.sub, 1, function(x)t.test(x)$p.value))
>
> On 0
apparently you want to check the genefilter package...
it defines functions like:
rowttests
colttests
rowFtests
colFtests
rowVars
rowSds
moreover, a quick look at Biobase is recommended...
that would save you lots of time as you wouldn't have to reinvent the
wheel.
b
On Mar 3, 2008, at
If I understand you correctly what you want to do is
do t-test (mu=0) for each column of the data.
Treating the data as a data.frame rather than a matrix
you can do something like this and then pick out the
p-values but with 140 t-tests I don't know what you'll
get in terms of anything meaninful.
You can try this:
cbind(data.sub, p.value=apply(data.sub, 1, function(x)t.test(x)$p.value))
On 03/03/2008, Keizer_71 <[EMAIL PROTECTED]> wrote:
>
> Hi Everyone,
>
> I need some simple help.
>
> Here are my codes
>
> ##will give me 1 probesets
> data.sub = data
Hi Everyone,
I need some simple help.
Here are my codes
##will give me 1 probesets
data.sub = data.matrix[order(variableprobe,decreasing=TRUE),][1:1,]
dim(data.sub)
data_output<-write.table(data.sub, file = "c://data_output.csv", sep = ",",
col.names = NA)
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