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Partial rewrite of the summary metrics aggregator to accumulate all metrics 

from sample-level summaries, rather than only specific metrics.  Continues to
manually handle fingerprinting.

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@6038 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
droazen 2011-06-22 22:53:53 +00:00
parent 4288ca1c24
commit 751aa8bfa6
1 changed files with 31 additions and 26 deletions
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57
python/generate_per_sample_metrics.py
View File

@ -14,6 +14,10 @@
# #
from java.lang import * from java.lang import *
from java.io import File,FileReader from java.io import File,FileReader
from edu.mit.broad.picard.genotype.concordance import DbSnpMatchMetrics
from net.sf.picard.analysis import AlignmentSummaryMetrics,InsertSizeMetrics
from net.sf.picard.analysis.directed import HsMetrics
from net.sf.picard.metrics import MetricsFile from net.sf.picard.metrics import MetricsFile
import os,string,sys import os,string,sys
@ -23,7 +27,7 @@ def median(l):
def mean(l): def mean(l):
return float(sum(l))/len(l) return float(sum(l))/len(l)
def get_metrics(filename): def get_all_metrics(filename):
if not os.path.exists(filename): if not os.path.exists(filename):
return None return None
file_reader = FileReader(filename) file_reader = FileReader(filename)
@ -33,6 +37,19 @@ def get_metrics(filename):
file_reader.close() file_reader.close()
return metrics return metrics
def get_sample_summary_metrics_fields(type):
return [field.getName() for field in type.getFields() if not field.getName().startswith('__')]
def get_sample_summary_metrics(filename):
if not os.path.exists(filename):
return None
file_reader = FileReader(filename)
metrics_file = MetricsFile()
metrics_file.read(file_reader)
metrics = metrics_file.getMetrics()[0]
file_reader.close()
return metrics
if len(sys.argv) != 2: if len(sys.argv) != 2:
print 'USAGE: %s <pipeline_file.yaml>' print 'USAGE: %s <pipeline_file.yaml>'
sys.exit(1) sys.exit(1)
@ -42,12 +59,14 @@ if not os.path.exists(sys.argv[1]):
bam_list_filename = sys.argv[1] bam_list_filename = sys.argv[1]
header = ['sample','HAPLOTYPES_CONFIDENTLY_MATCHING.MIN','HAPLOTYPES_CONFIDENTLY_MATCHING.MAX','HAPLOTYPES_CONFIDENTLY_MATCHING.MEDIAN', sample_summary_metrics_types = [ (HsMetrics,'hybrid_selection_metrics'),
'BAIT_SET','GENOME_SIZE','PCT_SELECTED_BASES','MEAN_TARGET_COVERAGE','ZERO_CVG_TARGETS_PCT','FOLD_80_BASE_PENALTY','PCT_TARGET_BASES_2X', (AlignmentSummaryMetrics, 'alignment_summary_metrics'),
'PCT_TARGET_BASES_10X','PCT_TARGET_BASES_20X','PCT_TARGET_BASES_30X','HS_LIBRARY_SIZE','PCT_PF_READS_ALIGNED','PF_HQ_ERROR_RATE', (InsertSizeMetrics, 'insert_size_metrics'),
'PF_INDEL_RATE','MEAN_READ_LENGTH','BAD_CYCLES','STRAND_BALANCE','PCT_CHIMERAS','PCT_ADAPTER','MEDIAN_INSERT_SIZE','TOTAL_SNPS'] (DbSnpMatchMetrics, 'dbsnp_matches') ]
data = ['%s'] * len(header)
header = ['sample','HAPLOTYPES_CONFIDENTLY_MATCHING.MIN','HAPLOTYPES_CONFIDENTLY_MATCHING.MAX','HAPLOTYPES_CONFIDENTLY_MATCHING.MEDIAN']
for metric_type in sample_summary_metrics_types:
header.extend(get_sample_summary_metrics_fields(metric_type[0]))
print string.join(header,'\t') print string.join(header,'\t')
# get a representative BAM file for each sample, to use as a base path. Note that this assumes every sample corresponds to the same base path. # get a representative BAM file for each sample, to use as a base path. Note that this assumes every sample corresponds to the same base path.
@ -66,7 +85,7 @@ bam_list.close()
for sample_id,filename in samples.items(): for sample_id,filename in samples.items():
basepath = filename[:filename.rindex('.bam')] basepath = filename[:filename.rindex('.bam')]
fingerprinting_summary_metrics = get_metrics('%s.%s' % (basepath,'fingerprinting_summary_metrics')) fingerprinting_summary_metrics = get_all_metrics('%s.%s' % (basepath,'fingerprinting_summary_metrics'))
if fingerprinting_summary_metrics != None: if fingerprinting_summary_metrics != None:
haplotypes_confidently_matching = [metric.HAPLOTYPES_CONFIDENTLY_MATCHING for metric in fingerprinting_summary_metrics] haplotypes_confidently_matching = [metric.HAPLOTYPES_CONFIDENTLY_MATCHING for metric in fingerprinting_summary_metrics]
@ -78,23 +97,9 @@ for sample_id,filename in samples.items():
max_haplotypes_confidently_matching = 'NA' max_haplotypes_confidently_matching = 'NA'
median_haplotypes_confidently_matching = 'NA' median_haplotypes_confidently_matching = 'NA'
insert_size_metrics = get_metrics('%s.%s' % (basepath,'insert_size_metrics')) data = [sample_id,min_haplotypes_confidently_matching,max_haplotypes_confidently_matching,median_haplotypes_confidently_matching]
if insert_size_metrics != None: for metrics_type,metrics_extension in sample_summary_metrics_types:
median_insert_size = insert_size_metrics[0].MEDIAN_INSERT_SIZE metrics = get_sample_summary_metrics('%s.%s' % (basepath,metrics_extension))
else: data.extend([getattr(metrics, metrics_field_name) for metrics_field_name in get_sample_summary_metrics_fields(metrics_type)])
median_insert_size = 'NA' print string.join(['%s']*len(header),'\t')%tuple(data)
hybrid_selection_metrics = get_metrics('%s.%s' % (basepath,'hybrid_selection_metrics'))[0]
alignment_summary_metrics = get_metrics('%s.%s' % (basepath,'alignment_summary_metrics'))[0]
dbsnp_matches = get_metrics('%s.%s' % (basepath,'dbsnp_matches'))[0]
print string.join(data,'\t')%(sample_id,min_haplotypes_confidently_matching,max_haplotypes_confidently_matching,median_haplotypes_confidently_matching,
hybrid_selection_metrics.BAIT_SET,hybrid_selection_metrics.GENOME_SIZE,hybrid_selection_metrics.PCT_SELECTED_BASES,
hybrid_selection_metrics.MEAN_TARGET_COVERAGE,hybrid_selection_metrics.ZERO_CVG_TARGETS_PCT,hybrid_selection_metrics.FOLD_80_BASE_PENALTY,
hybrid_selection_metrics.PCT_TARGET_BASES_2X,hybrid_selection_metrics.PCT_TARGET_BASES_10X,hybrid_selection_metrics.PCT_TARGET_BASES_20X,
hybrid_selection_metrics.PCT_TARGET_BASES_30X,hybrid_selection_metrics.HS_LIBRARY_SIZE,alignment_summary_metrics.PCT_PF_READS_ALIGNED,
alignment_summary_metrics.PF_HQ_ERROR_RATE,alignment_summary_metrics.PF_INDEL_RATE,alignment_summary_metrics.MEAN_READ_LENGTH,
alignment_summary_metrics.BAD_CYCLES,alignment_summary_metrics.STRAND_BALANCE,alignment_summary_metrics.PCT_CHIMERAS,
alignment_summary_metrics.PCT_ADAPTER,median_insert_size,dbsnp_matches.TOTAL_SNPS)