R help - Feb 2010 - paired wilcox test on each row of a large dataframe

hI
I have to calculate V statistic for each row of a large dataframe (28000). I
can not use multtest package for paired wilcox test. I have been using for
loop which are. Is there a way to speed the computation with another method
like using apply or tapply? 
My data set looks like this:
                 11573_MB   11911_MB   11966_MB   12091_MB  12168_MB  
12420_MB................
cg00000292 0.62123125 0.82663502 0.74687013 0.61774927 0.7337809 0.73203721
cg00002426 0.63631315 0.64408750 0.61975158 0.72500713 0.5753110 0.65146526
cg00003994 0.05035499 0.05189776 0.05882848 0.11198073 0.1313330 0.03883439
cg00005847 0.13936423 0.14967690 0.31874454 0.15876243 0.1111117 0.15070058
cg00006414 0.09059770 0.09915681 0.09952658 0.13955982 0.1757718 0.07566312
cg00007981 0.05622769 0.04143790 0.07167018 0.08051046 0.1378107 0.05439999
  ..............  11573_CB   11911_CB   11966_CB   12091_CB   12168_CB 
12420_CB
cg00000292 0.83059018 0.65396035 0.74519819 0.76007659 0.70335691 0.7857631
cg00002426 0.61450928 0.59160923 0.69857198 0.73028911 0.71808719 0.6741295
cg00003994 0.04223668 0.07910444 0.05416764 0.06156407 0.06381321 0.0643354
cg00005847 0.13897704 0.06407313 0.20449931 0.15683154 0.18936196 0.1610695
cg00006414 0.06520757 0.12243180 0.11380134 0.10957321 0.15759518 0.1236715
cg00007981 0.04789030 0.11699024 0.07143036 0.05996888 0.10829510 0.1069037
.
..
.
.
.
There are 12 columns and 27000 rows. I have to perform paired test on each
row (1:6 vs 7:12) and store the p value and statistic in two columns . Whats
the fastest way?
Gaurav Bhatti

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gauravbhatti <gaurav15984 <at> hotmail.com> writes:
> 
> 
> hI
> I have to calculate V statistic for each row of a large dataframe
> (28000). I can not use multtest package for paired wilcox test. I have
> been using for loop which are. Is there a way to speed the computation
> with another method like using apply or tapply?
Using a for loop is fine here (and basically unavoidable). If you need
it to be faster, use a matrix rather than a data.frame. (i.e. make a
matrix containing columns 1-12, which are all numeric and so do not need
to be in a data frame).

Below are versions using apply, sapply and an explicit for loop. There's
not much difference in speed. But the last one, in which the data is in
a data.frame with rownames, is much slower.

> d <- matrix(rnorm(12000), nrow=1000)
> system.time(ans <- apply(d, 1, function(row)
unlist(wilcox.test(row[1:6],
row[7:12])[c("p.value","statistic")])))
   user  system elapsed 
  2.660   0.064   2.730 
> system.time(ans2 <- sapply(1:nrow(d), function(i)
unlist(wilcox.test(d[i,1:6],
d[i,7:12])[c("p.value","statistic")])))
   user  system elapsed 
  2.480   0.108   2.583 
> system.time({ans3 <- matrix(nrow=nrow(d), ncol=2) ;
for(i in 1:nrow(d)) {
ans3[i,] <- unlist(wilcox.test(d[i,1:6], d[i,7:12])
[c("p.value","statistic")])}})
   user  system elapsed 
  2.504   0.000   2.503 
> d <- as.data.frame(d)
> rownames(d) <- paste(letters, 1:nrow(d))
> system.time(ans2 <- sapply(1:nrow(d), function(i)
unlist(wilcox.test(as.numeric(d[i,1:6]),
as.numeric(d[i,7:12]))[c("p.value","statistic")])))
   user  system elapsed 
  5.673   0.212   5.885 

Dan

> My data set looks like this:
>                  11573_MB   11911_MB   11966_MB   12091_MB  12168_MB  
> 12420_MB................
> cg00000292 0.62123125 0.82663502 0.74687013 0.61774927 0.7337809 0.73203721
> cg00002426 0.63631315 0.64408750 0.61975158 0.72500713 0.5753110 0.65146526
> cg00003994 0.05035499 0.05189776 0.05882848 0.11198073 0.1313330 0.03883439
> cg00005847 0.13936423 0.14967690 0.31874454 0.15876243 0.1111117 0.15070058
> cg00006414 0.09059770 0.09915681 0.09952658 0.13955982 0.1757718 0.07566312
> cg00007981 0.05622769 0.04143790 0.07167018 0.08051046 0.1378107 0.05439999
>   ..............  11573_CB   11911_CB   11966_CB   12091_CB   12168_CB 
> 12420_CB
> cg00000292 0.83059018 0.65396035 0.74519819 0.76007659 0.70335691 0.7857631
> cg00002426 0.61450928 0.59160923 0.69857198 0.73028911 0.71808719 0.6741295
> cg00003994 0.04223668 0.07910444 0.05416764 0.06156407 0.06381321 0.0643354
> cg00005847 0.13897704 0.06407313 0.20449931 0.15683154 0.18936196 0.1610695
> cg00006414 0.06520757 0.12243180 0.11380134 0.10957321 0.15759518 0.1236715
> cg00007981 0.04789030 0.11699024 0.07143036 0.05996888 0.10829510 0.1069037
> .
> ..
> .
> .
> .
> There are 12 columns and 27000 rows. I have to perform paired test on each
> row (1:6 vs 7:12) and store the p value and statistic in two columns .
Whats
> the fastest way?
> Gaurav Bhatti
>