How to perform wilcox test in R? - r

Question

I have this data frame with 4 genes and 3 samples measured in duplicate.
The TS is the standard.
I want to perform the wilcox test between the sample S1 with TS and S2 with the TS for each protein, but i´m having problems with the for cycle.
MS.rawMV <- read.table("C:/Users/aaa/Desktop/genomic/MS.csv", header=T)
S1_1 S1_2 S2_1 S2_2 TS_1 TS_2
gene 1 1 1 2 3 5 5
gene 2 10 10 4 5 9 10
gene 3 5 6 4 4 5 7
gene 4 9 9 8 7 6 6
Samples=list(
S1=grep("S1_*", colnames(MS.rawMV), value=TRUE),
S2=grep("S2_*", colnames(MS.rawMV), value=TRUE),
TS=grep("TS_*", colnames(MS.rawMV), value=TRUE))
sample.names <- names(Samples)
ref.sample <- "TS_"
# Build a data.frame
GRates <- data.frame(MS.rawMV[Reduce("c", Samples)])
## Statistics: non parametric test using TS as a standart
for (i in names(Samples)) {
WILCOXTEST <- wilcox.test(GRates[c(Samples[[i]])],Samples[[ref.sample]])
pnames <- paste(i,".wilcoxtest",sep="")
GRates[pnames] <- WILCOXTEST["p.value"]
}
Error in wilcox.test.default(GRates[Samples[[i]]], Samples[[ref.sample[i]]]) :
'x' must be numeric

Answer 1

It looks like the data is being treated as a factor.
The easiest fix would be to convert them back to numeric via factor->character->numeric.
try this
wilcox.test(
as.numeric(as.character(GRates[c(Samples[[i]])])),
as.numeric(as.character(Samples[[ref.sample]]))
)
If you try to convert straight to numeric from factor, you'll end up with integers that represent the factor classes instead of the actual values.

Answer 2

#DWin's comment is well taken (you have additional structure in your data that is hard to incorporate into a Wilcoxon test). However, if you want to ignore the distinction between the _1 and _2 columns and run Wilcoxon test on S1 vs TS and S2 vs TS, here's a way to rearrange the data and do it:
dat <- read.table(text="
gene S1_1 S1_2 S2_1 S2_2 TS_1 TS_2
1 1 1 2 3 5 5
2 10 10 4 5 9 10
3 5 6 4 4 5 7
4 9 9 8 7 6 6",
header=TRUE)
library(reshape2)
library(plyr)
m1 <- melt(dat,id.var="gene")
## break var_num into separate components
m2 <- subset(data.frame(m1,
colsplit(m1$variable,"_",names=c("var","num"))),
select=-variable)
## combine treatments with standards
m3 <- merge(subset(m2,var!="TS"),
subset(m2,var=="TS"),by=c("gene","num"))
## clean up
m4 <- subset(rename(m3,c(value.x="value",var.x="var",value.y="standard")),
select=-var.y)
## apply Wilcoxon test to each component, save the p value
ddply(m4,"var",
function(x) with(x,wilcox.test(value,standard))$p.value)
Or, if you want to test each replication separately (as in #agstudy's answer), do
ddply(m4,c("var","num"),
function(x) with(x,wilcox.test(value,standard))$p.value)
instead.

Answer 3

I think , since wilcox.test is not vectorized you need 2 loops. Even I am not sure Of the statistical meaning of this , here how you can do :
nn <- colnames(dat)
lapply(1:2,function(x){
col.L <- grep(paste0('S',x,'_*'),nn)
col.R <- dat[,paste0('TS_',x)]
lapply(col.L,function(y)
wilcox.test(dat[,y],col.R)['p.value'])
})
Here I assume dat as
dat <- read.table(text='S1_1 S1_2 S2_1 S2_2 TS_1 TS_2
gene_1 1 1 2 3 5 5
gene_2 10 10 4 5 9 10
gene_3 5 6 4 4 5 7
gene_4 9 9 8 7 6 6',header=TRUE)