How to implement R's p.adjust in Python

Question

I have a list of p-values and I would like to calculate the adjust p-values for multiple comparisons for the FDR. In R, I can use:

pval <- read.csv("my_file.txt",header=F,sep="\t")
pval <- pval[,1]
FDR <- p.adjust(pval, method= "BH")
print(length(pval[FDR<0.1]))
write.table(cbind(pval, FDR),"pval_FDR.txt",row.names=F,sep="\t",quote=F )

How can I implement this code in Python? Here was my feable attempt in Python with the help of Google:

pvalue_list [2.26717873145e-10, 1.36209234286e-11 , 0.684342083821...] # my pvalues
pvalue_lst = [v.r['p.value'] for v in pvalue_list]
p_adjust = R.r['p.adjust'](R.FloatVector(pvalue_lst),method='BH')
for v in p_adjust:
    print v

The above code throws an AttributeError: 'float' object has no attribute 'r' error. Can anyone help point out my problem? Thanks in advance for the help!

Answer 1

If you wish to be sure of what you are getting from R, you can also indicate that you wish to use the function in the R package 'stats':

from rpy2.robjects.packages import importr
from rpy2.robjects.vectors import FloatVector

stats = importr('stats')

p_adjust = stats.p_adjust(FloatVector(pvalue_list), method = 'BH')