I have a table like this:
chr start end con1_1_1 con1_2_1 con1_3_1 con2_1_4 con2_2_4 con2_3_4
1 1 1 7512 0.45180723 0.21982759 0.06666667 0.4105960 0.1024735 0.2284710
2 1 13169 20070 0.07142857 0.77631579 0.90434783 0.1363636 0.8985507 0.6033058
3 1 36598 37518 0.13750000 0.43300248 0.09113300 0.9612403 0.1233596 0.7459016
4 1 37512 40365 0.64940239 0.95954693 0.46091644 0.7251656 0.1325648 0.4121901
5 1 40359 48801 0.09504132 0.96491228 0.15428571 0.6388889 0.5165165 0.8050847
6 1 77084 83129 0.91773779 0.28978224 0.56115108 0.9587302 0.5469256 0.6995614
My data are in 2 conditions with 3 replications in each condition. I would like for each row to run a Kruskal-Wallis rank sum test, which means that in each row the con1 (with 3 values) will be tested with con2 (with 3 values). This is what I tried to do but I don't know if it works correctly and if the selection is right.
for (j in 1:len) {
data=newdata[len,]
flabel<-factor(c(rep("con1",3),rep("con2",3)))
data1=c(data[,4],data[,5],data[,6],data[,7],data[,8],data[,9])
datav=data.frame(flabel,data1)
test=kruskal.test(data1 ~ flabel, data = datav)
print(test$p.value)
}
Do you know any faster or more elegant way to do the test for each row?
You can try to create your own function, and apply it to each row. broom::tidy could help:
library(broom)
# function that put the second 3 cols vs the third 3 cols of the dats,
# using the kruskal test
fun <- function(x) {
tidy(kruskal.test(list(x[4:6],x[7:9])))
}
apply(dats, 1, fun)
Using the tidy function, you can also do this:
# store the result as a list
test <- apply(dats, 1, fun)
# "flat" it
test <- do.call(rbind,test)
cbind(dats,test)
chr start end con1_1_1 con1_2_1 con1_3_1 con2_1_4 con2_2_4 con2_3_4 statistic p.value parameter
1 1 1 7512 0.45180723 0.2198276 0.06666667 0.4105960 0.1024735 0.2284710 0.04761905 0.8272593 1
2 1 13169 20070 0.07142857 0.7763158 0.90434783 0.1363636 0.8985507 0.6033058 0.04761905 0.8272593 1
3 1 36598 37518 0.13750000 0.4330025 0.09113300 0.9612403 0.1233596 0.7459016 1.19047619 0.2752335 1
4 1 37512 40365 0.64940239 0.9595469 0.46091644 0.7251656 0.1325648 0.4121901 1.19047619 0.2752335 1
5 1 40359 48801 0.09504132 0.9649123 0.15428571 0.6388889 0.5165165 0.8050847 0.42857143 0.5126908 1
6 1 77084 83129 0.91773779 0.2897822 0.56115108 0.9587302 0.5469256 0.6995614 0.42857143 0.5126908 1
method
1 Kruskal-Wallis rank sum test
2 Kruskal-Wallis rank sum test
3 Kruskal-Wallis rank sum test
4 Kruskal-Wallis rank sum test
5 Kruskal-Wallis rank sum test
6 Kruskal-Wallis rank sum test
It seems that you have the same results 2 by two:
# first row
x <-c( 0.07142857, 0.7763158, 0.90434783)
y <- c ( 0.1363636, 0.8985507, 0.6033058)
kruskal.test(list(x,y))
Kruskal-Wallis rank sum test
data: list(x, y)
Kruskal-Wallis chi-squared = 0.047619, df = 1, p-value = 0.8273
# second row
x <-c( 0.45180723, 0.2198276, 0.06666667)
y <- c (0.4105960, 0.1024735, 0.2284710)
kruskal.test(list(x,y))
Kruskal-Wallis rank sum test
data: list(x, y)
Kruskal-Wallis chi-squared = 0.047619, df = 1, p-value = 0.8273
With data:
dats <- read.table(text ="chr start end con1_1_1 con1_2_1 con1_3_1 con2_1_4 con2_2_4 con2_3_4
1 1 1 7512 0.45180723 0.21982759 0.06666667 0.4105960 0.1024735 0.2284710
2 1 13169 20070 0.07142857 0.77631579 0.90434783 0.1363636 0.8985507 0.6033058
3 1 36598 37518 0.13750000 0.43300248 0.09113300 0.9612403 0.1233596 0.7459016
4 1 37512 40365 0.64940239 0.95954693 0.46091644 0.7251656 0.1325648 0.4121901
5 1 40359 48801 0.09504132 0.96491228 0.15428571 0.6388889 0.5165165 0.8050847
6 1 77084 83129 0.91773779 0.28978224 0.56115108 0.9587302 0.5469256 0.6995614", header = T)