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Merge pull request #38 from hexavier/master
Incorporate crosstalks analysis into mim-tRNAseq
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# -*- coding: utf-8 -*- | ||
""" | ||
Created on Wed Oct 13 11:01:53 2021 | ||
@author: Xavier Hernandez-Alias | ||
""" | ||
import pandas as pd | ||
import numpy as np | ||
from os import listdir | ||
from shutil import rmtree | ||
from os.path import isdir,isfile,join | ||
from scipy.stats import fisher_exact | ||
from statsmodels.stats.multitest import multipletests | ||
import multiprocessing | ||
from functools import partial | ||
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# Define function for parallelization | ||
def analyze_1sample(s,dirpath,thres): | ||
outdf = pd.DataFrame(columns=["sample","ref","var1","var2","pval","odds_ratio","values"]) | ||
n=0 | ||
ref_files = [f for f in listdir(join(dirpath, s)) if isfile(join(dirpath, s, f))] | ||
for f in ref_files: | ||
# Load table | ||
ref = f[:-7].replace(".","/") | ||
ref = "-".join(ref.split("-")[:-1]) if not "chr" in ref and not "/" in ref else ref | ||
readsdf = pd.read_csv(join(dirpath, s, f),sep="\t",compression="gzip",index_col="READ",dtype="category") | ||
pairs = [] | ||
for v1 in readsdf.columns: | ||
for v2 in readsdf.columns: | ||
if v1!=v2 and set([v1,v2]) not in pairs: | ||
pairs.append(set([v1,v2])) | ||
reads1 = readsdf[v1].dropna() | ||
reads2 = readsdf[v2].dropna() | ||
# Do test only if at least % positions are modified | ||
if reads1.shape[0]>0 and reads2.shape[0]>0: | ||
if sum(reads1!="0")/reads1.shape[0]>thres and sum(reads2!="0")/reads2.shape[0]>thres: | ||
# Keep only reads that contain both v1 and v2 | ||
tempdf = readsdf[[v1,v2]].dropna(how="any") | ||
# Build contingency table, var1 in rows and var 2 in columns | ||
counts = (tempdf!="0").value_counts() | ||
if counts.shape[0]==4: | ||
cont_tab = np.array([[counts.loc[True,True], counts.loc[True,False]], | ||
[counts.loc[False,True], counts.loc[False,False]]]) | ||
#p = chi2_contingency(cont_tab)[1] | ||
oddsr, p = fisher_exact(cont_tab) | ||
outdf.loc[n] = [s,ref,v1,v2,p,oddsr,counts] | ||
n += 1 | ||
# Remove temporary files | ||
rmtree(join(dirpath, s)) | ||
return outdf | ||
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def crosstalks_wrapper(dirpath, thres, threads): | ||
# Multiprocessed function | ||
pool = multiprocessing.Pool(threads) | ||
frozen_fun = partial(analyze_1sample, dirpath=dirpath, thres=thres) | ||
samples = [f for f in listdir(dirpath) if isdir(join(dirpath, f))] | ||
dfs = pool.map(func=frozen_fun, iterable=samples, chunksize=1) | ||
pool.close() | ||
pool.join() | ||
# Concatenate tables | ||
outdf = pd.concat(dfs, ignore_index=True) | ||
# Correct multiple comparisons | ||
outdf["pval_corrected"] = multipletests(outdf["pval"].values,method="fdr_bh")[1] | ||
# Include canonical positions | ||
posinfo = pd.read_csv("/".join(dirpath.split("/")[:-1])+"/mods/mismatchTable.csv", sep="\t", | ||
index_col=["isodecoder","pos"],usecols=["isodecoder","pos","canon_pos"], | ||
dtype="category") | ||
posinfo = posinfo[~posinfo.index.duplicated(keep='first')] | ||
outdf["canon_var1"] = ["Charged" if s[1]=="Charged" else posinfo.loc[(s[0],s[1]),"canon_pos"] if s[0] in posinfo.index.get_level_values(0) else "NA" for s in outdf[["ref","var1"]].to_numpy()] | ||
outdf["canon_var2"] = ["Charged" if s[1]=="Charged" else posinfo.loc[(s[0],s[1]),"canon_pos"] if s[0] in posinfo.index.get_level_values(0) else "NA" for s in outdf[["ref","var2"]].to_numpy()] | ||
# Save table | ||
outdf.to_csv(dirpath+"/crosstalks.tsv",sep="\t",index=False) | ||
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