def readGWAS(args):
start = timer()
validate(args)
regexp = re.compile(args.gwas_file_pattern) if args.gwas_file_pattern else None
names = Utilities.contentsWithRegexpFromFolder(args.gwas_folder, regexp)
names.sort() #cosmetic, because different filesystems/OS yield folders in different order
if len(names) == 0:
msg = "No GWAS files found on %s with pattern %s" % (args.gwas_folder, args.gwas_file_pattern,)
raise Exceptions.ReportableException(msg)
print "INFO: Reading GWAS data"
gwas_format = GWASUtilities.gwas_format_from_args(args)
GWAS.validate_format_basic(gwas_format)
GWAS.validate_format_for_strict(gwas_format)
#model = PredictionModel.load_model(args.model_db_path) if args.model_db_path else None
model = None
# dataframe
r = pandas.DataFrame()
for name in names:
b = build_betas(args, model, gwas_format, name)
r = pandas.concat([r,b])
end = timer()
logging.info("Successfully parsed input gwas in %s seconds"%(str(end-start)))
print("Successfully parsed input gwas in %s seconds"%(str(end-start)))
return r
def run(self):
if self.args.weight_db_path:
logging.info("Loading weight model")
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(
self.weight_db_path)
else:
weight_db_logic = None
names = Utilities.contentsWithRegexpFromFolder(self.gwas_folder,
self.gwas_regexp)
if not os.path.exists(self.output_folder):
os.makedirs(self.output_folder)
if len(names) == 0:
raise Exceptions.ReportableException(
"No GWAS files found on %s with pattern %s" % (
self.gwas_folder,
self.gwas_regexp.pattern,
))
for name in names:
try:
self.buildBetas(weight_db_logic, name)
# This just means that there is some extra stuff inside that directory,
# so I'm thinking we want to ignore it.
except Exceptions.BadFilename as e:
logging.info("Wrong file name: %s, skipping", e.msg)
pass
def run(args):
start = timer()
validate(args)
if args.gwas_folder:
regexp = re.compile(
args.gwas_file_pattern) if args.gwas_file_pattern else None
names = Utilities.contentsWithRegexpFromFolder(args.gwas_folder,
regexp)
names.sort(
) #cosmetic, because different filesystems/OS yield folders in different order
if len(names) == 0:
msg = "No GWAS files found on %s with pattern %s" % (
args.gwas_folder,
args.gwas_file_pattern,
)
raise Exceptions.ReportableException(msg)
else:
names = [args.gwas_file]
gwas_format = GWASUtilities.gwas_format_from_args(args)
GWAS.validate_format_basic(gwas_format)
GWAS.validate_format_for_strict(gwas_format)
model = PredictionModel.load_model(
args.model_db_path,
args.model_db_snp_key) if args.model_db_path else None
if args.output_folder:
if not os.path.exists(args.output_folder):
os.makedirs(args.output_folder)
for name in names:
output_path = os.path.join(args.output_folder, name)
if not ".gz" in output_path:
output_path += ".gz"
if os.path.exists(output_path):
logging.info(
"%s already exists, delete it if you want it to be done again",
output_path)
continue
b = build_betas(args, model, gwas_format, name)
c = "gzip" if ".gz" in name else None
b.to_csv(output_path, sep="\t", index=False, compression=c)
end = timer()
logging.info("Successfully ran GWAS input processing in %s seconds" %
(str(end - start)))
else:
r = pandas.DataFrame()
for name in names:
b = build_betas(args, model, gwas_format, name)
r = pandas.concat([r, b])
end = timer()
logging.info("Successfully parsed input gwas in %s seconds" %
(str(end - start)))
return r
def expression_from_args(args, prediction_results = None):
if prediction_results:
if isinstance(prediction_results, BasicPredictionRepository):
logging.info("Preparing PrediXcan context from data")
expression = Expression.ExpressionFromData(prediction_results.genes)
else:
raise Exceptions.ReportableException("Invalid prediction results")
elif args.hdf5_expression_file:
logging.info("Preparing PrediXcan HDF5 context")
expression = HDF5Expression.Expression(args.hdf5_expression_file)
elif args.expression_file:
logging.info("Preparing PrediXcan context")
expression = PlainTextExpression.Expression(args.expression_file)
else:
raise RuntimeError("Could not build context from arguments")
return expression
