BH correction procedure giving monotonous/repeatitive adjusted p-values

Question

I was performing a basic R analysis on a gene expression data. The analysis aims at finding whether there is a gender difference in the gene expression for adrenal gland.

The data were separated as males and females and later t-tests were performed. Finally, I got a set of p-values and a BH correction procedure was performed on it. But the adjusted p-values I get, are monotonous, same value repeating from beginning to end. And I cannot find any rejections at 10 percent significance level. What could be possibly wrong?

Here is the R code performed for the t-test on gene expression data:

first.row <- t.test(son.a[1,males],son.a[1,-males])  

# Result from t-test :
# Welch Two Sample t-test
# data: son.a[1, males] and son.a[1, -males]
# t = 0.8923, df = 9.594, p-value = 0.3941
# alternative hypothesis: true difference in means is
#       not equal to 0
#       95 percent confidence interval:
#        -0.1188546 0.2761207
#        sample estimates:
#        mean of x mean of y
#        0.527884 0.449251

' son.a ' is the dataframe consisting of gene expression values of 42421 genes. The samples are taken from adrenal glands of both males and females (a total of 9 samples).

# Assigning a function 't.test.pvalue'
t.test.pvalue <- function(dat) {
    results <- t.test(dat[males],dat[-males])
    return(results$p.value)
    }

t.test.pvalue(son.a[1,])
# [1] 0.3940679





# Applying t.test.pvalue to all 42421 rows  
all.rows <- apply(son.a,1,t.test.pvalue)  

head(all.rows)
# [1] 0.3940679 0.5616102 0.6953087 0.3064443 0.8942156 0.8191188  

tail(all.rows)  
# [1] 0.8631147 0.3911861 0.4482372 0.8286146 0.8603733 0.2700229


# Loading "mutoss"
library("mutoss")

# Applying BH function
bh.adjustment <- BH(all.rows,alpha=0.1)
# Benjamini-Hochberg's (1995) step-up procedure
# Number of hyp.:    42421 
# Number of rej.:    0 

# Using p.adjust function
adjP <- p.adjust(all.rows,method = "BH")
adjP  
# [1] 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772
 0.9999772 0.9999772
# [10] 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772
# [19] 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 0.9999772 

The values are the same till the end of the vector 'adjP'.Anything wrong in the above R code?

Thanks in advance!

Answer 1

This is not a "problem" per se. Please have a look at these two posts for details on BH correction at CrossValidated and StackOverflow .

The data you have (assuming that it is properly preprocessed / normalized) suggests that there is no significant difference based on sex. There might be other confounding effects that you need to take into account though (batch, age, disease status, etc...).