Added ParseDCCSequenceData.py to repository and made changes that allow an analysis of quantity of sequence data by platform and project, moved table / record system to a new module called FlatFileTable.py and built that into ParseDCCSequenceData and CoverageEval.py; changed lod threshold in CoverageEvalWalker.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1201 348d0f76-0448-11de-a6fe-93d51630548a
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andrewk
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@ -88,7 +88,7 @@ public class CoverageEvalWalker extends LocusWalker<List<String>, String> {
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LocusContext subContext = new LocusContext(context.getLocation(), sub_reads, sub_offsets);
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AlleleFrequencyEstimate alleleFreq = SSG.map(tracker, ref, subContext);
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if (alleleFreq != null && (alleleFreq.lodVsRef >= LOD_THRESHOLD || alleleFreq.lodVsRef <= LOD_THRESHOLD)) {
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if (alleleFreq != null) {
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GenotypeCalls.add(coverage+"\t"+coverage_available+"\t"+hc_genotype+"\t"+alleleFreq.callType()+"\t"+alleleFreq.asGeliString());
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}
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}
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@ -1,13 +1,6 @@
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#!/usr/bin/env python
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import sys
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def chopped_line_generator(filename):
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fin = open(filename)
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fin.readline() # pull off header
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for line in fin:
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line = line.rstrip()
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yield line
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import sys, itertools, FlatFileTable
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def subset_list_by_indices(indices, list):
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subset = []
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@ -15,59 +8,56 @@ def subset_list_by_indices(indices, list):
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subset.append(list[index])
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return subset
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def chunk_generator(line_gen, key_fields):
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def chunk_generator(record_gen, key_fields):
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"""Input:
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line_gen: generator that produces lines with linefeeds chopped off
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key_fields: field numbers in each record used to determine chunk membership
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Output:
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locus_chunk: list of consecutive lines that have the same key_fields
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"""
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locus_chunk: list of consecutive lines that have the same key_fields"""
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locus_chunk = []
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last_key = ""
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first_line = True
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for line in line_gen:
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fields = line.split()
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key = subset_list_by_indices(key_fields, fields)
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if key == last_key or first_line:
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locus_chunk.append(line)
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first_line = False
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first_record = True
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for record in record_gen:
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key = [record[f] for f in key_fields]
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if key == last_key or first_record:
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locus_chunk.append(record)
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first_record = False
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else:
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if locus_chunk != []:
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yield locus_chunk
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locus_chunk = [line]
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locus_chunk = [record]
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last_key = key
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yield locus_chunk
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def chunk_stats(chunk):
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records = 0
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conf_calls = 0
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correct_genotype = 0
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for record in chunk:
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fields = record.split()
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if fields[2] == fields[9]:
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correct_genotype += 1
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if abs(float(record["BtnbLod"])) >= 5:
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conf_calls += 1
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if record["HapmapChipGenotype"] == record["BestGenotype"]:
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correct_genotype += 1
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records += 1
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return float(correct_genotype) / records
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return float(correct_genotype) / max(conf_calls,1), float(conf_calls) / max(records,1)
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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sys.exit("Usage: CoverageEval.py geli_file")
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filename = sys.argv[1]
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fin = open(filename)
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locus_gen = chunk_generator(chopped_line_generator(filename), (4,5))
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locus_gen = chunk_generator(FlatFileTable.record_generator(filename, None), ("Sequence","Position"))
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print "Fraction correct genotype\tCoverage sampled\tLocus\tReference base\tHapmap chip genotype (Max. coverage genotype call for reference calls)"
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for locus in locus_gen:
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#print "NEW LOCUS"
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covs = dict()
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coverage_chunk_gen = chunk_generator(locus, (0,4,5))
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coverage_chunk_gen = chunk_generator(locus, ("DownsampledCoverage", "Sequence", "Position"))
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for cov_chunk in coverage_chunk_gen:
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#print "NEW COVERAGE"
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#print "\n".join(cov_chunk)
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fields = cov_chunk[0].split()
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coverage = fields[1]
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print "\t".join(map(str,("%.2f"%chunk_stats(cov_chunk), coverage, fields[4]+":"+fields[5],fields[6],fields[2])))
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#covs[coverage] = cov_chunk
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record = cov_chunk[0]
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print "\t".join(map(str,("%.4f\t%.4f"%chunk_stats(cov_chunk), record["DownsampledCoverage"], record["Sequence"]+":"+record["Position"],record["ReferenceBase"],record["HapmapChipGenotype"])))
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@ -0,0 +1,19 @@
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#!/usr/bin/env python
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import sys, itertools
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def record_generator(filename, sep="\t"):
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"""Given a file with field headers on the first line and records on subsequent lines,
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generates a dictionary for each line keyed by the header fields"""
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fin = open(filename)
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header = fin.readline().rstrip().split() # pull off header
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for line in fin:
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fields = line.rstrip().split(sep)
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record = dict(itertools.izip(header, fields))
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yield record
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def record_matches_values(record, match_field_values):
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for match_field, match_values in match_field_values:
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if record[match_field] not in match_values:
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return False
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return True
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@ -0,0 +1,75 @@
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#!/usr/bin/env python
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import operator, FlatFileTable
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class db_file:
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filename = ""
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def __init__(self, filenm):
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self.filename = filenm
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def count_fields(self, fixed_field, fixed_field_values, count_field):
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record_gen = FlatFileTable.record_generator(self.filename)
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counts = dict()
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#fixed_field_num = self.field_names[fixed_field]
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print count_field+" for "+fixed_field+" = "+" or ".join(fixed_field_values)
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for record in record_gen:
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if record[fixed_field] in fixed_field_values:
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#fixed_field_value = fields[fixed_field_num]
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count_field_num = record[count_field]
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if counts.has_key(count_field_num):
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counts[count_field_num] += 1
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else:
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counts[count_field_num] = 0
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for k,v in sorted(counts.items(), key=operator.itemgetter(1), cmp=lambda x,y: y-x ):
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print str(k)+"\t"+str(v)
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def count_bases(self, fixed_field_values): #, fixed_field_values):
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record_gen = FlatFileTable.record_generator(self.filename)
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base_count = 0
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#fixed_field_num = self.field_names[fixed_field]
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#print "For "+fixed_field+" = "+" or ".join(fixed_field_values)+":",
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print "For "+ " AND ".join( [one_ffv[0]+" = "+" OR ".join(one_ffv[1]) for one_ffv in fixed_field_values] )
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for record in record_gen:
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#if record[fixed_field] in fixed_field_values:
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if FlatFileTable.record_matches_values(record, fixed_field_values):
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try:
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base_count += int(record["BASE_COUNT"])
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except ValueError:
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pass
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print "%e bases" % base_count
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if __name__ == "__main__":
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db = db_file("sequence.index")
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platforms = (("ILLUMINA",), ("AB SOLiD","SOLID","ABI_SOLID","AB SOLiD System 2.0"), ("LS454",))
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studies = (("1000Genomes Project Pilot 1",), ("1000Genomes Project Pilot 2",), ("1000Genomes Project Pilot 3",))
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for select_field, select_field_values in (): #(("INSTRUMENT_PLATFORM", platforms), ("STUDY_NAME", studies)):
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for count_field in ("CENTER_NAME", "STUDY_NAME", "INSTRUMENT_PLATFORM"):
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for select_field_value in select_field_values:
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db.count_fields(select_field, select_field_value, count_field)
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for select_field_value in select_field_values:
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db.count_bases(((select_field, select_field_value),))
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print
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for field1, value1 in zip(["INSTRUMENT_PLATFORM"]*len(platforms), platforms):
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for field2, value2 in zip(["STUDY_NAME"]*len(studies), studies):
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db.count_bases(((field1, value1), (field2, value2)))
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