CODEWITHSUNDEEP
rapplyp-value
soso
soso

Reputation: 11

Mann-Whitney-U test

I have a csv with several thousands of samples whose gene expression after different treatments should be compared:

ID    U1         U2        U3        H1        H2        H3
1     5.95918   6.07211   6.01437   5.89113   5.89776   5.95443
2     6.56789   5.98897   6.67844   5.78987   6.01789   6.12789
..

I was asked to do a Mann Whitney u test and R is giving me results when I use this:

results <- apply(data,1,function(x){wilcox.test(x[1:3],x[4:6])$pvalue})

However, I just get values like 0.1 or 0.5..

When I added alternative ="greater" I got values like 0.35000 or 0.05000 and a few samples got pvalues like 0.14314 (that's a value I am okay with). So I am wondering why R is giving me such strange pvalues (0.35000,..) and how I can fix it to get "normal" pvalues.

Upvotes: 1

Views: 1967

Answers (1)

Roland
Roland

Reputation: 132959

You are doing a non-parametric test, where the test statistics is derived from the ranks. With a sample size of 3, there are just a few possible distinct values for the test statistics.

Example:

set.seed(42)
x <- matrix(rnorm(3000), ncol=6)
ps <- apply(x, 1, function(a) wilcox.test(a[1:3], a[4:6])$p.value)
table(ps)
#ps
#0.1 0.2 0.4 0.7   1 
# 54  45 108 141 152

Upvotes: 5

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