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fit_as_coefficients.py
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fit_as_coefficients.py
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# Copyright 2013 Graham McVicker and Bryce van de Geijn
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
import math
import gzip
import argparse
from scipy.optimize import *
from scipy.special import betaln
import scipy.stats
import numpy as np
import util
def parse_options():
parser = argparse.ArgumentParser(description="This script estimates the "
"overdispersion parameter for the allele-specific "
"(Beta-Binomial) half of the combined halotype test."
" A single overdispersion parameter is estimated for "
"each individual (across sites), under the assumption "
"that all sites come from the null hypothesis (no "
"genetic association)");
parser.add_argument("--read_error_rate", "-e", action='store', dest='read_error_rate',
help="sequence read error rate (default=0.005)",
type=float, default=0.005)
parser.add_argument("infile_list",
help="Path to file containing list of CHT input "
"files (one for each individual)",
action='store', default=None)
parser.add_argument("out_file",
help="File to write overdispersion parameter estimates to",
action='store', default=None)
return parser.parse_args()
def open_input_files(in_filename):
if not os.path.exists(in_filename) or not os.path.isfile(in_filename):
sys.stderr.write("input file %s does not exist or is not a regular file\n" %
in_filename)
exit(2)
# read file that contains list of input files
in_file = open(in_filename)
infiles = []
for line in in_file:
# open each input file and read first line
filename = line.rstrip()
if not filename or not os.path.exists(filename) or not os.path.isfile(filename):
sys.stderr.write("input file '%s' does not exist or is not a regular file\n"
% line)
exit(2)
if util.is_gzipped(filename):
f = gzip.open(filename, "rt")
else:
f = open(filename)
# skip header
f.readline()
infiles.append(f)
in_file.close()
if len(infiles) == 0:
sys.stderr.write("no input files specified in file '%s'\n" % options.infile_list)
exit(2)
return infiles
def main():
options = parse_options()
infiles = open_input_files(options.infile_list)
outfile = open(options.out_file,"w")
dup_snp_warn = True
# read input data and estimate dispersion coefficient
# for one individual at a time
for i in range(len(infiles)):
cur_file = infiles[i]
AS_ref = []
AS_alt = []
hetps =[]
header = cur_file.readline()
# combine allele-specific read counts into one large
# array for this individual
for line in cur_file:
snpinfo = line.strip().split()
if snpinfo[12] != "NA":
# read information aout target SNPs
snp_locs = np.array([int(y.strip()) for y in snpinfo[9].split(';')],
dtype=np.int32)
snp_as_ref = np.array([int(y) for y in snpinfo[12].split(';')],
dtype=np.int32)
snp_as_alt = np.array([int(y) for y in snpinfo[13].split(';')],
dtype=np.int32)
snp_hetps = np.array([float(y.strip()) for y in snpinfo[10].split(';')],
dtype=np.float64)
# same SNP should not be provided multiple times, this
# can create problems with combined test. Warn and filter
# duplicate SNPs
uniq_loc, uniq_idx = np.unique(snp_locs, return_index=True)
if dup_snp_warn and uniq_loc.shape[0] != snp_locs.shape[0]:
sys.stderr.write("WARNING: discarding SNPs that are repeated "
"multiple times in same line\n")
dup_snp_warn = False
AS_ref.extend(snp_as_ref[uniq_idx])
AS_alt.extend(snp_as_alt[uniq_idx])
hetps.extend(snp_hetps[uniq_idx])
AS_ref = np.array(AS_ref)
AS_alt = np.array(AS_alt)
hetps = np.array(hetps)
# find maximu likelihood estimate for overdispersion parameter
res = minimize_scalar(likelihood, bounds=(0.01, 0.99),
args=(AS_ref, AS_alt, hetps, options.read_error_rate),
options = {'xatol' : 0.001},
method="Bounded")
LL = res.fun
dispersion = res.x
sys.stderr.write("AS dispersion[%d]: %g\n" % (i, dispersion))
sys.stderr.write("LL[%d]: -%g\n" % (i, LL))
outfile.write(str(dispersion)+"\n")
outfile.flush()
def likelihood(dispersion, AS_ref, AS_alt, hetps, error):
cur_like = 0
for i in range(len(AS_ref)):
# calculate likelihood for each heterozygous site, under
# assumption that true reference proportion is 50%
cur_like += AS_betabinom_loglike([math.log(0.5), math.log(0.5)],
dispersion, AS_ref[i],
AS_alt[i], hetps[i], error)
return -cur_like
def addlogs(loga, logb):
"""Helper function: perform numerically-stable addition in log space"""
return max(loga, logb) + math.log(1 + math.exp(-abs(loga - logb)))
#Given parameters, returns log likelihood. Note that some parts have been cancelled out
def AS_betabinom_loglike(logps, sigma, AS1, AS2, hetp, error):
if sigma >= 1.0 or sigma <= 0.0:
return -99999999999.0
a = math.exp(logps[0] + math.log(1/sigma**2 - 1))
b = math.exp(logps[1] + math.log(1/sigma**2 - 1))
part1 = 0
part1 += betaln(AS1 + a, AS2 + b)
part1 -= betaln(a, b)
if hetp == 1:
return part1
e1 = math.log(error) * AS1 + math.log(1 - error) * AS2
e2 = math.log(error) * AS2 + math.log(1 - error) * AS1
if hetp == 0:
return addlogs(e1, e2)
return addlogs(math.log(hetp)+part1, math.log(1-hetp) + addlogs(e1,e2))
main()