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20100303a.py
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20100303a.py
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"""
Analyze nucleotide sites of a pileup using hmmus. [UNFINISHED]
Parameters x, y, and z are branch lengths which define the two three-taxon
trees whose Jukes-Cantor nucleotide distribution at the tips
define the {RR, RA, AA, AB} zygosity distributions for the
recent vs. ancient mrca states.
The low, medium, and high parameters are expectations of three geometrically
distributed mixture components of a garbage state.
The seqerror parameter is the probability of sequencing randomization;
this the probability that the sequencing machine spits out a random
nucleotide instead of the correct nucleotide.
The nomcoverage parameter defines the nominal coverage of the pileup.
The kmulticoverages parameter defines the number of
nominal coverage multiples which might result from duplications.
"""
from StringIO import StringIO
import itertools
import math
import argparse
import hmmus
import scipy.misc
from SnippetUtil import HandlingError
import Form
import FormOut
import DGRP
import ReadCoverageRef
import Util
import ExternalHMM
import lineario
import TransitionMatrix
import const
g_default_data = const.read('20100729a')
g_default_params = [
('x', '0.1'),
('y', '0.01'),
('z', '0.0001'),
('low', '2'),
('med', '20'),
('high', '1000'),
('seqerr', '.01'),
('nomcoverage', '20'),
('kmulticoverages', '4')]
def get_form():
"""
@return: the body of a form
"""
data_lines = ['\t'.join(str(x) for x in row) for row in g_default_data]
form_objects = [
Form.Integer('region_size', 'expected region size',
10, low=1, high=1000000),
Form.Float('misalignment_effect', 'misalignment effect',
'0.5', low_inclusive=0),
Form.MultiLine('param_field', 'parameters',
'\n'.join('\t'.join(p) for p in g_default_params)),
Form.MultiLine('data_field', 'data',
g_default_data.rstrip())]
return form_objects
def get_form_out():
return FormOut.Image('plot')
def get_response_content(fs):
"""
@param fs: a FieldStorage object containing the cgi arguments
@return: a (response_headers, response_text) pair
"""
out = StringIO()
lines = Util.get_stripped_lines(StringIO(fs.param_field))
model = DGRP.Model()
model.from_lines(lines)
# see how the states interact with the observations
states = (
model.get_recent_state(),
model.get_ancient_state(),
model.get_misaligned_state(fs.misalignment_effect),
model.get_garbage_state())
# define the transition object
nstates = len(states)
prandom = min(1.0, (nstates / (nstates - 1.0)) / fs.region_size)
T = TransitionMatrix.UniformTransitionObject(prandom, nstates)
# use StringIO objects for storage
hmm = ExternalHMM.ExternalModel(T, states, (None, None, None))
converter = lineario.IntTupleConverter()
o_stream = lineario.SequentialStringIO(converter, fs.data_field)
hmm.init_dp(o_stream)
o_stream.open_read()
for p, obs in itertools.izip(hmm.posterior(), o_stream.read_forward()):
p_recent, p_ancient, p_misaligned, p_garbage = p
# get the prior probability of polymorphism conditional on state
p_recent_AA = states[0].get_posterior_distribution(obs)[2]
p_ancient_AA = states[1].get_posterior_distribution(obs)[2]
# compute the posterior probability of a polymorphism
posterior_polymorphism = 0
posterior_polymorphism += p_recent * p_recent_AA
posterior_polymorphism += p_ancient * p_ancient_AA
# Given that a polymorphism occurred,
# get the probability distribution over the
# three non-reference nucleotides.
r = model.seqerr
log_Pr = math.log(r/4.0)
log_PA = math.log(1 - 3*r/4.0)
logs = [
obs[1]*log_PA + obs[2]*log_Pr + obs[3]*log_Pr,
obs[1]*log_Pr + obs[2]*log_PA + obs[3]*log_Pr,
obs[1]*log_Pr + obs[2]*log_Pr + obs[3]*log_PA]
condmaxpost = math.exp(max(logs) - scipy.misc.logsumexp(logs))
# get the posterior probability distribution
maxpost = posterior_polymorphism * condmaxpost
# show the inference for this position
print >> out, obs, p, maxpost
o_stream.close()
return out.getvalue()
def main(args):
filenames = (args.out_forward, args.out_scaling, args.out_backward)
# aggregate and validate the model parameters
model = DGRP.Model()
model.from_fieldstorage(args)
# see how the states interact with the observations
states = (
model.get_recent_state(),
model.get_ancient_state(),
model.get_misaligned_state(args.misalignment_effect),
model.get_garbage_state())
# define the transition object
nstates = len(states)
prandom = min(1.0, (nstates / (nstates - 1.0)) / args.region_size)
T = TransitionMatrix.UniformTransitionObject(prandom, nstates)
# make the hmm
hmm = ExternalHMM.ExternalModel(T, states, filenames)
converter = lineario.IntTupleConverter()
o_stream = lineario.SequentialDiskIO(converter, args.obsfile)
hmm.init_dp(o_stream)
o_stream.open_read()
for p, obs in itertools.izip(hmm.posterior(), o_stream.read_forward()):
p_recent, p_ancient, p_misaligned, p_garbage = p
maxpost = get_maxpost(p_recent, p_ancient, p_misaligned, p_garbage)
# show the annotation for this position
annotation = list(obs) + list(p) + [maxpost]
print '\t'.join(str(x) for x in annotation)
o_stream.close()
def get_maxpost(p_recent, p_ancient, p_misaligned, p_garbage, seqerr, obs):
"""
@param p_recent: P(chromosomes are identical by descent here)
@param p_ancient: P(chromosomes are not identical by descent here)
@param p_misaligned: P(chromosomes are misaligned to reference sequence)
@param p_garbage: P(position is garbage)
@param seqerr: probability of sequencing randomization
@param obs: tuple of reference nt count and the three non-ref nt counts
"""
# get the prior probability of polymorphism conditional on state
p_recent_AA = states[0].get_posterior_distribution(obs)[2]
p_ancient_AA = states[1].get_posterior_distribution(obs)[2]
# compute the posterior probability of a polymorphism
posterior_polymorphism = 0
posterior_polymorphism += p_recent * p_recent_AA
posterior_polymorphism += p_ancient * p_ancient_AA
# Given that a polymorphism occurred,
# get the probability distribution over the
# three non-reference nucleotides.
r = model.seqerr
log_Pr = math.log(r/4.0)
log_PA = math.log(1 - 3*r/4.0)
logs = [
obs[1]*log_PA + obs[2]*log_Pr + obs[3]*log_Pr,
obs[1]*log_Pr + obs[2]*log_PA + obs[3]*log_Pr,
obs[1]*log_Pr + obs[2]*log_Pr + obs[3]*log_PA]
condmaxpost = math.exp(max(logs) - scipy.misc.logsumexp(logs))
# get the posterior probability distribution
maxpost = posterior_polymorphism * condmaxpost
return maxpost
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--force', action='store_true',
help='overwrite existing files')
parser.add_argument('--out_forward',
help='forward vectors are written to this file')
parser.add_argument('--out_backward',
help='backward vectors are written to this file')
parser.add_argument('--out_scaling',
help='scaling factors are written to this file')
parser.add_argument('--misalignment_effect', type=float, default=0.5,
help='misalignment branch length')
parser.add_argument('--x', type=float, default=0.1,
help='reference branch length')
parser.add_argument('--y', type=float, default=0.01,
help='line branch length')
parser.add_argument('--z', type=float, default=0.0001,
help='mutant branch length')
parser.add_argument('--low', type=int, default=2,
help='low random coverage per base')
parser.add_argument('--med', type=int, default=20,
help='medium random coverage per base')
parser.add_argument('--high', type=int, default=1000,
help='high random coverage per base')
parser.add_argument('--seqerr', type=float, default=0.1,
help='sequencing error')
parser.add_argument('--nomcoverage', type=int, default=20,
help='nominal total coverage per position')
parser.add_argument('--kmulticoverages', type=int, default=4,
help='allowed multiples of nominal coverage')
parser.add_argument('--region_size', type=int, default=1000,
help='expected contiguous region lengths')
parser.add_argument('obsfile')
args = parser.parse_args()
main(args)