zzh
/
gatk-3.8
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

57
python/generate_per_sample_metrics.py
View File

@ -14,6 +14,10 @@
#
from java.lang import *
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
import os,string,sys
@ -23,7 +27,7 @@ def median(l):
def mean(l):
return float(sum(l))/len(l)
def get_metrics(filename):
def get_all_metrics(filename):
if not os.path.exists(filename):
return None
file_reader = FileReader(filename)
@ -33,6 +37,19 @@ def get_metrics(filename):
file_reader.close()
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:
print 'USAGE: %s <pipeline_file.yaml>'
sys.exit(1)
@ -42,12 +59,14 @@ if not os.path.exists(sys.argv[1]):
bam_list_filename = sys.argv[1]
header = ['sample','HAPLOTYPES_CONFIDENTLY_MATCHING.MIN','HAPLOTYPES_CONFIDENTLY_MATCHING.MAX','HAPLOTYPES_CONFIDENTLY_MATCHING.MEDIAN',
'BAIT_SET','GENOME_SIZE','PCT_SELECTED_BASES','MEAN_TARGET_COVERAGE','ZERO_CVG_TARGETS_PCT','FOLD_80_BASE_PENALTY','PCT_TARGET_BASES_2X',
'PCT_TARGET_BASES_10X','PCT_TARGET_BASES_20X','PCT_TARGET_BASES_30X','HS_LIBRARY_SIZE','PCT_PF_READS_ALIGNED','PF_HQ_ERROR_RATE',
'PF_INDEL_RATE','MEAN_READ_LENGTH','BAD_CYCLES','STRAND_BALANCE','PCT_CHIMERAS','PCT_ADAPTER','MEDIAN_INSERT_SIZE','TOTAL_SNPS']
data = ['%s'] * len(header)
sample_summary_metrics_types = [ (HsMetrics,'hybrid_selection_metrics'),
(AlignmentSummaryMetrics, 'alignment_summary_metrics'),
(InsertSizeMetrics, 'insert_size_metrics'),
(DbSnpMatchMetrics, 'dbsnp_matches') ]
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')
# 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():
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:
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'
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:
median_insert_size = insert_size_metrics[0].MEDIAN_INSERT_SIZE
else:
median_insert_size = 'NA'
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)
for metrics_type,metrics_extension in sample_summary_metrics_types:
metrics = get_sample_summary_metrics('%s.%s' % (basepath,metrics_extension))
data.extend([getattr(metrics, metrics_field_name) for metrics_field_name in get_sample_summary_metrics_fields(metrics_type)])
print string.join(['%s']*len(header),'\t')%tuple(data)