def process_sample(parse_functions, sample, samples, config, amplicon_list):
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--samples_file',
help="Input configuration file for samples")
parser.add_argument('-c', '--configuration',
help="Configuration file for various settings")
parser.add_argument('-l', '--list',
help="List file of SNPs to process")
args = parser.parse_args()
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.samples_file, config)
sample_cov_data = defaultdict(lambda: defaultdict())
for sample in samples:
sys.stdout.write("Processing sample {}\n".format(sample))
sample_cov_data[sample] = process_sample(parse_functions, sample,
samples, config, snps)
sys.stdout.write("Writing out data\n")
with open("glioma_snp_data.txt", 'wb') as out:
out.write("SNP\tChr\tPos")
for sample in samples:
out.write("\t{} - AAF\t{} - Depth".format(sample, sample))
out.write("\n")
for snp in snps:
out.write("{}".format(snp))
for sample in samples:
if sample_snp_data[sample][snp]:
out.write("\t{}\t{}".format(sample_snp_data[sample][snp]['freq'],
sample_snp_data[sample][snp]['depth']))
else:
out.write("\t-\t-")
out.write("\n")
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--randseed', help="Seed number for reproducible sub-sampling")
parser.add_argument('-n', '--number', help="Number of iterations per sample to perform", default=1)
parser.add_argument('-s', '--samples_file', help="Input configuration file for samples")
parser.add_argument('-c', '--configuration', help="Configuration file for various settings")
parser.add_argument('-a', '--address', help="IP Address for Cassandra connection", default='127.0.0.1')
parser.add_argument('-u', '--username', help='Cassandra username for login', default=None)
argcomplete.autocomplete(parser)
Job.Runner.addToilOptions(parser)
args = parser.parse_args()
fractions = [50, 33, 25]
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.samples_file, config)
# Workflow Graph definition. The following workflow definition should create a valid Directed Acyclic Graph (DAG)
root_job = Job.wrapJobFn(pipeline.spawn_batch_jobs, cores=1)
if args.username:
password = getpass.getpass()
auth_provider = PlainTextAuthProvider(username=args.username, password=password)
else:
auth_provider = None
for sample in samples:
for fraction in fractions:
import argparse
import HTSeq
from collections import defaultdict
from ddb import configuration
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help="Input config file for samples")
parser.add_argument('-c',
'--configuration',
help="Configuration file for various settings")
parser.add_argument('-o', '--output', help="Output file name for CSV file")
args = parser.parse_args()
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.input, config)
transcript_counts = defaultdict(
lambda: defaultdict(lambda: defaultdict(int)))
for sample in samples:
sys.stderr.write("Processing sample {}\n".format(sample))
gtf_file = HTSeq.GFF_Reader(samples[sample]['gtf'], end_included=True)
for feature in gtf_file:
# sys.stderr.write("Processing entry: {}\n".format(feature))
if feature.type is 'transcript':
transcript_counts[feature.attr['transcript_id']][sample][
'FPKM'] = feature.attr['FPKM']
