Commit 20337ba3 authored by jhoogenboom's avatar jhoogenboom
Browse files

Introducing BGHomRaw.

* New tool BGHomRaw computes noise ratios for all detected noise in
  all homozygous reference samples. The idea is to plot this data
  in a visualisation that will be added later.
parent 513f967a
#!/usr/bin/env python
Compute noise ratios for all noise detected in homozygous reference
import argparse
from ..lib import pos_int_arg, add_input_output_args, get_input_output_files,\
add_allele_detection_args, parse_allelelist, parse_library,\
get_sample_data, add_sequence_format_args, adjust_stats,\
__version__ = "0.1dev"
# Default values for parameters are specified below.
# Default minimum amount of background to consider, as a percentage of
# the highest allele.
# This value can be overridden by the -m command line option.
# Default minimum number of reads to consider.
# This value can be overridden by the -n command line option.
# Default minimum number of samples for each true allele.
# This value can be overridden by the -s command line option.
# Default minimum number of samples required for each background product
# to be included in the analysis, as a percentage of the number of
# samples with a certain true allele.
# This value can be overridden by the -S command line option.
def add_sample_data(data, sample_data, sample_alleles, min_pct, min_abs, tag):
# Check presence of all alleles.
for marker in sample_alleles:
allele = sample_alleles[marker]
if (marker, allele) not in sample_data:
raise ValueError(
"Missing allele %s of marker %s!" % (allele, marker))
elif 0 in sample_data[marker, allele]:
raise ValueError(
"Allele %s of marker %s has 0 reads!" % (allele, marker))
# Enter the read counts into data and check the thresholds.
for marker, sequence in sample_data:
if marker not in sample_alleles:
# Sample does not participate in this marker.
allele = sample_alleles[marker]
factors = [100./x for x in sample_data[marker, allele]]
factors.append(100./sum(sample_data[marker, allele]))
if (marker, allele) not in data:
data[marker, allele] = {}
if sequence not in data[marker, allele]:
data[marker, allele][sequence] = {
"tag": [],
"forward": [],
"reverse": [],
"fnoise": [],
"rnoise": [],
"tnoise": [],
"passed_filter": 0}
data[marker, allele][sequence]["tag"].append(tag)
data[marker, allele][sequence]["forward"].append(
sample_data[marker, sequence][0])
data[marker, allele][sequence]["reverse"].append(
sample_data[marker, sequence][1])
data[marker, allele][sequence]["fnoise"].append(
sample_data[marker, sequence][0] * factors[0])
data[marker, allele][sequence]["rnoise"].append(
sample_data[marker, sequence][1] * factors[1])
data[marker, allele][sequence]["tnoise"].append(
sum(sample_data[marker, sequence]) * factors[2])
if sum(count >= min_abs and count*factor >= min_pct
for count, factor in
zip(sample_data[marker, sequence], factors[:2])):
data[marker, allele][sequence]["passed_filter"] += 1
def filter_data(data, min_samples, min_sample_pct):
Remove all alleles from data that have less than min_samples samples
and remove all data of sequences that don't pass the detection
thresholds in at least min_sample_pct per cent of the samples with a
particular allele.
for marker, allele in data.keys():
if data[marker, allele][allele]["passed_filter"] < min_samples:
del data[marker, allele]
factor = 100./data[marker, allele][allele]["passed_filter"]
for sequence in data[marker, allele].keys():
if (data[marker, allele][sequence]["passed_filter"] * factor <
del data[marker, allele][sequence]
def compute_ratios(samples_in, outfile, allelefile, annotation_column, min_pct,
min_abs, min_samples, min_sample_pct, seqformat, library,
# Parse library and allele list.
library = parse_library(library) if library is not None else None
allelelist = {} if allelefile is None \
else parse_allelelist(allelefile, seqformat, library)
# Read sample data.
data = {}
lambda tag, sample_data: add_sample_data(
data, sample_data,
{m: allelelist[tag][m].pop() for m in allelelist[tag]},
min_pct, min_abs, tag),
allelelist, annotation_column, seqformat, library, marker, True)
# Ensure minimum number of samples per allele and filter
# insignificant background products.
filter_data(data, min_samples, min_sample_pct)
outfile.write("\t".join(["sample", "marker", "allele", "sequence",
"forward", "reverse", "total", "fnoise", "rnoise", "tnoise"]) + "\n")
for marker, allele in data:
for sequence in data[marker, allele]:
for i in range(len(data[marker, allele][sequence]["tag"])):
data[marker, allele][sequence]["tag"][0], marker, allele,
sequence] + [
str(x) if abs(x) > 0.0000000001 else "0" for x in (
data[marker, allele][sequence]["forward"][i],
data[marker, allele][sequence]["reverse"][i],
data[marker, allele][sequence]["forward"][i] +
data[marker, allele][sequence]["reverse"][i],
data[marker, allele][sequence]["fnoise"][i],
data[marker, allele][sequence]["rnoise"][i],
data[marker, allele][sequence]["tnoise"][i])]) + "\n")
def add_arguments(parser):
filtergroup = parser.add_argument_group("filtering options")
filtergroup.add_argument('-m', '--min-pct', metavar="PCT", type=float,
help="minimum amount of background to consider, as a percentage "
"of the highest allele (default: %4.2f)" % _DEF_THRESHOLD_PCT)
filtergroup.add_argument('-n', '--min-abs', metavar="N", type=pos_int_arg,
help="minimum amount of background to consider, as an absolute "
"number of reads (default: %(default)s)")
filtergroup.add_argument('-s', '--min-samples', metavar="N",
help="require this minimum number of samples for each true allele "
"(default: %(default)s)")
filtergroup.add_argument('-S', '--min-sample-pct', metavar="PCT",
help="require this minimum number of samples for each background "
"product, as a percentage of the number of samples with a "
"particular true allele (default: %(default)s)")
filtergroup.add_argument('-M', '--marker', metavar="MARKER",
help="work only on MARKER")
def run(args):
files = get_input_output_files(args)
if not files:
raise ValueError("please specify an input file, or pipe in the output "
"of another program")
compute_ratios(files[0], files[1], args.allelelist, args.annotation_column,
args.min_pct, args.min_abs, args.min_samples,
args.min_sample_pct, args.sequence_format, args.library,
def main():
Main entry point.
parser = argparse.ArgumentParser(
except OSError as error:
if __name__ == "__main__":
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