parser.add_argument("--expression_folder", help="Folder with predicted gene expressions. (plain text file format)")
parser.add_argument("--expression_pattern", help="Patterns to select expression files", default=None)
parser.add_argument("--input_phenos_file", help="Text file (or gzip-compressed) where one column will be used as phenotype")
parser.add_argument("--simulation_type", help="What kind of genotype to simulate: [random, combination, simple]")
parser.add_argument("--output_prefix", help="File where stuff will be saved.")
parser.add_argument("--verbosity", help="Log verbosity level. 1 is everything being logged. 10 is only high level messages, above 10 will hardly log anything", default = "10")
parser.add_argument("--throw", action="store_true", help="Throw exception on error", default=False)
parser.add_argument("--code_999", help="values of -999 in expression are to be ignored", action="store_true", default=False)
parser.add_argument("--mode", help="Type of regression. Can be: {}".format(MultiPrediXcanAssociation.MTPMode.K_MODES), default=MultiPrediXcanAssociation.MTPMode.K_LINEAR)
parser.add_argument("--pc_condition_number", help="Principal components condition number", type=int)
parser.add_argument("--pc_eigen_ratio", help="Principal components filter, cutoff at proportion to max eigenvalue", type=float)
parser.add_argument("--standardize_expression", help="Standardise input predicted expressions.", action="store_true", default=False)
parser.add_argument("--only_truth", help="Run Multi-PrediXcan only with selected causal models.", action="store_true", default=False)
parser.add_argument("--simulation_parameters", help="Depends on particular scheme", action="append", nargs=2)
parser.add_argument("--do_predixcan", help="Also compute predixcan association", action="store_true", default=False)
parser.add_argument("--max_n_results", help="Optional. If provided, run up to as many analysis", type=int)
args = parser.parse_args()
Logging.configureLogging(int(args.verbosity))
if args.throw:
run(args)
else:
try:
run(args)
except Exceptions.ReportableException as e:
logging.error(e.msg)
except Exception as e:
logging.info("Unexpected error: %s" % str(e))
import argparse
parser = argparse.ArgumentParser(
description=
'PrediXcanAssociation.py %s: Single-Tissue PrediXcan association' %
(metax.__version__))
add_arguments(parser)
parser.add_argument(
"--verbosity",
help=
"Log verbosity level. 1 is everything being logged. 10 is only high level messages, above 10 will hardly log anything",
default=10)
parser.add_argument("--throw",
action="store_true",
help="Throw exception on error",
default=False)
args = parser.parse_args()
Logging.configureLogging(int(args.verbosity))
if args.throw:
run(args)
else:
try:
run(args)
except Exceptions.ReportableException as e:
logging.error(e.msg)
except Exception as e:
logging.info("Unexpected error: %s" % str(e))
for i, gene in enumerate(data.columns.values[2:]):
p[i, :] = data[gene].to_numpy()
g[i] = np.string_(gene)
logging.info("saving samples")
s = f.create_dataset("samples", (n_samples, ), dtype="S25")
for i in xrange(0, n_samples):
s[i] = np.string_(data["IID"][i])
f.close()
logging.info("Done")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser("""Convert expression text file to HDF5.
WARNING: h5py _cache has been deprecated.
Input format should be a tab-separated text file like so:
FID IID GENE1 GENE2 ...
I1 I1 0.1 0.2 ...
I2 I2 0.0 -0.1 ...
....
""")
parser.add_argument(
"--input", default="Text file with predicted transcriptomic trait")
parser.add_argument("--output")
parser.add_argument("--verbosity", default=logging.INFO, type=int)
args = parser.parse_args()
Logging.configureLogging(args.verbosity)
run(args)
help="exclude some genes from a mystical list.",
action="store_true",
default=False)
parser.add_argument("--gene_digest_file",
help="path of gene information digest",
default="data/gencode.v18.genes.patched_contigs.summary.protein")
parser.add_argument("--predixcan_file",
help="name of PrediXcan results in data folder",
default="data/PrediXcan_T1D_DGNWholeBlood_EN0.5.txt")
parser.add_argument("--zscore_file",
help="File with zscore results in results folder",
default='results/zscores.csv')
parser.add_argument("--output",
help="File with merged zscore and predixcan results",
default="results/T1DWB_predixcan_zscore.csv")
parser.add_argument("--save_error_proxy",
help="Output beta error proxy",
action="store_true",
default=False)
args = parser.parse_args()
Logging.configureLogging(logging.INFO)
work = PostProcessData(args)
work.run()
continue
var = numpy.var(snp.data, ddof=1)
line = ",".join([snp.name, str(float(var))])+"\n"
file.write(line)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Build variances from PHASE3 data and weights database.')
parser.add_argument("--weight_db",
help="name of weight db in data folder")
#,default="data_folder/DGN-WB_0.5.db")
parser.add_argument("--phase_folder",
help="name of folder containing PHASE 3 data",
default="intermediate/TGF_EUR")
parser.add_argument("--output_file",
help="name of folder to dump results in",
default="intermediate/var/var.txt.gz")
args = parser.parse_args()
Logging.configureLogging(7)
work = CalculateVariances(args)
work.run()
def post_process(study, reference):
command = "PostProcessData.py"
command += " --predixcan_file " + PREDIXCAN[study]
command += " --zscore_file " + zscore_path(study, reference)
command += " --output " + predixcan_zscore_path(study, reference)
command += " --save_error_proxy"
logging.log(9, command)
call(command.split())
#
def plot(study, reference):
command = "Rscript PlotValuesWithSamples.R"
command += " --zscore_file " + zscore_path(study, reference).split(".csv")[0]
command += " --predixcan_file " + predixcan_zscore_path(study, reference).split(".csv")[0]
logging.log(9, command)
call(command.split())
def run():
for reference in REF_POP:
for study in STUDY_POP:
zscore(study, reference)
post_process(study,reference)
#plot(study, reference)
if __name__ == "__main__":
Logging.configureLogging(9)
run()
continue
var = numpy.var(snp.data, ddof=1)
line = ",".join([snp.name, str(float(var))]) + "\n"
file.write(line)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description='Build variances from PHASE3 data and weights database.')
parser.add_argument("--weight_db", help="name of weight db in data folder")
#,default="data_folder/DGN-WB_0.5.db")
parser.add_argument("--phase_folder",
help="name of folder containing PHASE 3 data",
default="intermediate/TGF_EUR")
parser.add_argument("--output_file",
help="name of folder to dump results in",
default="intermediate/var/var.txt.gz")
args = parser.parse_args()
Logging.configureLogging(7)
work = CalculateVariances(args)
work.run()