def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t",
"--test",
dest="test",
type="string",
help="supply help")
parser.add_option("--plot-type",
dest="plot_type",
type="choice",
choices=["manhattan", "qqplot", "epistasis"],
help="plot type to generate")
parser.add_option("--resolution",
dest="resolution",
type="choice",
choices=["genome_wide", "chromosome", "fine_map"],
help="the resolution of plotting, wether the plot "
"depicts the whole genome, a single chromosome or "
"a specific locus")
parser.add_option("--file-format",
dest="file_format",
type="choice",
choices=["plink", "cassi", "cassi_covar"],
help="input file format, used to parse the file "
"properly")
parser.add_option("--save-path",
dest="save_path",
type="string",
help="path and filename to save image to")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(resolution="genome_wide",
plot_type="manhattan",
file_format="plink")
# if the input is a list of files, split them
infile = argv[-1]
infiles = infile.split(",")
# need to parse epistasis output slightly differently
if options.plot_type == "epistasis":
epi = True
else:
epi = False
if len(infiles) > 1:
results = gwas.GWASResults(assoc_file=infiles,
epistasis=epi,
file_format=options.file_format)
elif len(infiles) == 1:
results = gwas.GWASResults(assoc_file=infile,
epistasis=epi,
file_format=options.file_format)
else:
raise IOError("no input files detected, please specifiy association "
"results files as the last command line argument")
if options.plot_type == "manhattan":
df = results.plotManhattan(resolution=options.resolution,
save_path=options.save_path)
elif options.plot_type == "qqplot":
results.plotQQ(save_path=options.save_path,
resolution=options.resolution)
elif options.plot_type == "epistasis":
results.plotEpistasis(save_path=options.save_path,
resolution=options.resolution)
else:
pass
# only output appended results for Manhattan plot, not qqplot
try:
df.to_csv(options.stdout, sep="\t", index=None)
except UnboundLocalError:
pass
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--task", dest="task", type="choice",
choices=["get_hits", "extract_results",
"merge_freq"],
help="task to perform")
parser.add_option("--p-threshold", dest="p_threshold", type="float",
help="threshold for association p-value, below "
"which results will be output")
parser.add_option("--output-directory", dest="outdir", type="string",
help="output file directory")
parser.add_option("--snp-set", dest="snpset", type="string",
help="file containing list of SNP per row to "
"extract from GWAS results")
parser.add_option("--frequency-directory", dest="freq_dir", type="string",
help="Directory containing plink .frq files corresponding"
" to all chromosomes")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# if the input is a list of files, split them
infile = argv[-1]
infiles = infile.split(",")
if len(infiles) > 1:
results = gwas.GWASResults(assoc_file=infiles)
elif len(infiles) == 1:
results = gwas.GWASResults(assoc_file=infile)
else:
raise IOError("no input files detected, please specifiy association "
"results files as the last command line argument")
if options.task == "get_hits":
hits = results.getHits(float(options.p_threshold))
for name, region in hits:
try:
try:
top_reg = region.sort_values(by="CHISQ",
ascending=False)
top_bp = top_reg.iloc[0]["BP"]
top_snp = top_reg.iloc[0]["SNP"]
except KeyError:
top_reg = region
top_reg.loc[:, "STAT"] = abs(top_reg["STAT"])
top_reg = top_reg.sort_values(by="STAT",
ascending=False)
top_bp = top_reg.iloc[0]["BP"]
top_snp = top_reg.iloc[0]["SNP"]
except KeyError:
top_reg = region
top_reg.loc[:, "STAT"] = abs(top_reg["T"])
top_reg = top_reg.sort_values(by="T",
ascending=False)
top_bp = top_reg.iloc[0]["BP"]
top_snp = top_reg.iloc[0]["SNP"]
outname = "_".join(["chr%s" % str(name),
str(top_bp),
top_snp,
"significant"])
outfile = outname + ".tsv"
out_file = "/".join([options.outdir, outfile])
E.info("output association results from Chr%s to %s" %
(str(name), out_file))
# this keeps outputing the first column as unamed: 0,
# need to remove this
try:
if region.columns[0] != "A1":
region.drop([region.columns[0]], inplace=True, axis=1)
except:
pass
region.to_csv(out_file, sep="\t", index=None)
elif options.task == "extract_results":
with IOTools.openFile(options.snpset, "r") as sfile:
snpset = sfile.readlines()
snpset = [snp.rstrip("\n") for snp in snpset]
snp_df = results.extractSNPs(snpset)
snp_df.dropna(axis=0, how='all', inplace=True)
snp_df.drop_duplicates(subset=["SNP"], inplace=True)
snp_df.to_csv(options.stdout, sep="\t", index=None)
elif options.task == "merge_freq":
# sequentially merge GWAS result with frequency data
# to make file for GCTA joint analysis
regex = re.compile("(\S+).frq$")
cojo_df = results.mergeFrequencyResults(options.freq_dir,
file_regex=regex)
cojo_df.to_csv(options.stdout, sep="\t", index=None)
else:
pass
# write footer and output benchmark information.
E.Stop()