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#!/usr/bin/env python
"""Calculate differentially expressed genes using EdgeR from bioconductor.
Usage: <count_file>
import os
import sys
import csv
import collections
import numpy
import rpy2.robjects as robjects
import rpy2.robjects.numpy2ri
def main(count_file):
base, ext = os.path.splitext(count_file)
outfile = "%s-diffs.csv" % (base)
counts = read_count_file(count_file)
data, groups, sizes, conditions, genes = edger_matrices(counts)
probs = run_edger(data, groups, sizes, genes)
write_outfile(outfile, genes, conditions, counts, probs)
def write_outfile(outfile, genes, conditions, work_counts, probs):
with open(outfile, "w") as out_handle:
writer = csv.writer(out_handle)
writer.writerow(["Region"] +
["%s count" % c for c in conditions] + ["edgeR p-value"])
out_info = []
for i, gene in enumerate(genes):
counts = [int(work_counts[c][gene]) for c in conditions]
out_info.append((probs[i], [gene] + counts))
[writer.writerow(start + [prob]) for prob, start in out_info]
def run_edger(data, groups, sizes, genes):
"""Call edgeR in R and organize the resulting differential expressed genes.
# find the version we are running -- check for edgeR exactTest function
is_13_plus = True
except LookupError:
is_13_plus = False
params = {'group' : groups, 'lib.size' : sizes}
dgelist = robjects.r.DGEList(data, **params)
# 1.3+ version has a different method of calling and retrieving p values
if is_13_plus:
# perform Poisson adjustment and assignment as recommended in the manual
robjects.globalEnv['dP'] = dgelist
msP <- de4DGE(dP, doPoisson = TRUE)
dP$pseudo.alt <- msP$pseudo
dP$common.dispersion <- 1e-06
dP$conc <- msP$conc
dP$common.lib.size <- msP$M
dgelist = robjects.globalEnv['dP']
de = robjects.r.exactTest(dgelist)
tags = robjects.r.topTags(de, n=len(genes))
tag_table = tags[0]
indexes = [int(t) - 1 for t in tag_table.rownames()]
# can retrieve either raw or adjusted p-values
#pvals = list(tags.r['p.value'][0])
pvals = list(tag_table.r['adj.p.val'][0])
# older 1.2 version of edgeR
ms = robjects.r.deDGE(dgelist, doPoisson=True)
tags = robjects.r.topTags(ms, pair=groups, n=len(genes))
indexes = [int(t) - 1 for t in tags.rownames()]
# can retrieve either raw or adjusted p-values
#pvals = list(tags.r['P.Value'][0])
pvals = list(tags.r['adj.P.Val'][0])
assert len(indexes) == len(pvals)
pvals_w_index = zip(indexes, pvals)
assert len(pvals_w_index) == len(indexes)
return [p for i,p in pvals_w_index]
def get_conditions_and_genes(work_counts):
conditions = work_counts.keys()
all_genes = []
for c in conditions:
all_genes = list(set(all_genes))
sizes = [work_counts[c]["Total"] for c in conditions]
return conditions, all_genes, sizes
def edger_matrices(work_counts):
"""Retrieve matrices for input into edgeR differential expression analysis.
conditions, all_genes, sizes = get_conditions_and_genes(work_counts)
assert len(sizes) == 2
groups = [1, 2]
data = []
final_genes = []
for g in all_genes:
cur_row = [int(work_counts[c][g]) for c in conditions]
if sum(cur_row) > 0:
return (numpy.array(data), numpy.array(groups), numpy.array(sizes),
conditions, final_genes)
def read_count_file(in_file):
"""Read count information from a simple CSV file into a dictionary.
counts = collections.defaultdict(dict)
with open(in_file) as in_handle:
reader = csv.reader(in_handle)
header =
conditions = header[1:]
for parts in reader:
region_name = parts[0]
region_counts = [float(x) for x in parts[1:]]
for ci, condition in enumerate(conditions):
counts[condition][region_name] = region_counts[ci]
return dict(counts)
if __name__ == "__main__":
if len(sys.argv) != 2:
print __doc__