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("--method",
dest="method",
type="choice",
choices=["cases_explained", "probability_phenotype"],
help="Which results to report, either the proportion "
"of cases explained or the probability of the "
"phenotype given the number of alleles carried")
parser.add_option("--map-file",
dest="map_file",
type="string",
help="plink .map file with SNP positions")
parser.add_option("--ped-file",
dest="ped_file",
type="string",
help="plink ped file with phenotype and "
"genotype data - A2 major allele coded")
parser.add_option("--gwas-file",
dest="gwas",
type="string",
help="gwas results file, assumes Plink "
"output format. Must contain SNP, BP, "
"OR column headers. Assumes results relate "
"to the A1 allele")
parser.add_option("--flip-alleles",
dest="flip",
action="store_true",
help="force alleles to flip if OR < 1")
parser.add_option("--plot-statistic",
dest="plot_stat",
type="choice",
choices=["frequency", "cumulative"],
help="plot either cases frequency or cumulative "
"frequency of cases")
parser.add_option("--plot-path",
dest="plot_path",
type="string",
help="save path for plot")
parser.add_option("--flag-explained-recessive",
dest="explained",
action="store_true",
help="flag individuals explained by carriage of "
"2 risk alleles - NOT IMPLIMENTED")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# required files are .ped file, .map file and gwas results file
E.info("reading GWAS results file: %s" % options.gwas)
snp_df = pd.read_table(options.gwas, sep="\t", header=0, index_col=None)
snp_list = snp_df["SNP"].values
# parse ped file
E.info("Reading ped file: %s" % options.ped_file)
ped_df = gwas.parsePed(options.ped_file, compound_geno=True)
# parse map file and get SNP indices that correspond to
# ped file genotypes
E.info("Fetching SNPs from map file: %s" % options.map_file)
snp_index = gwas.getSNPs(options.map_file, snp_list)
E.info("SNPs found: %i" % len(snp_index))
# extract SNPs and ORs as key, value pairs
or_dict = snp_df.loc[:, ["SNP", "OR"]].to_dict(orient='list')
snp_or = dict(zip(or_dict["SNP"], or_dict["OR"]))
if options.flip:
E.info("Flipping major alleles to risk alleles")
flipped_genos = gwas.flipRiskAlleles(snp_index=snp_index,
snp_results=snp_or,
genos=ped_df["GENOS"].tolist())
# merge flipped genotypes with pedigree frame to get phenotypes
geno_df = pd.DataFrame(flipped_genos, index=ped_df["FID"])
else:
# split genos into a dataframe
genos = np.array(ped_df["GENOS"].tolist())
geno_df = pd.DataFrame(genos, index=ped_df["FID"])
merged = pd.merge(geno_df, ped_df, left_index=True, right_on="FID")
# need to discount missing genotypes > 1%
# frequencies of number of risk alleles by trait frequency
E.info("count #risk alleles per individual")
risk_results = gwas.countRiskAlleles(ped_frame=merged,
snp_index=snp_index.values(),
report=options.method,
flag=options.explained)
risk_freqs = risk_results["freqs"]
cumulative = risk_results["cumulative"]
# select results upto and including cumulative freq = 1.0
max_indx = [fx for fx, fy in enumerate(cumulative) if fy == 1.0][0]
max_freqs = risk_freqs[:max_indx + 1]
max_cum = cumulative[:max_indx + 1]
bins = [ix for ix, iy in enumerate(cumulative)][:max_indx + 1]
# plot!
if options.plot_stat == "frequency":
E.info("Generating plot of #risk alleles vs. P(Phenotype)")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_freqs,
savepath=options.plot_path,
ytitle="P(Phenotype)")
elif options.plot_stat == "cumulative":
E.info("Generating plot of #risk alleles vs. cumulative frequency")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_cum,
savepath=options.plot_path,
ytitle="Cumulative frequency cases")
hist_df["freq"] = risk_results["freqs"][:max_indx + 1]
hist_df["cumulative"] = risk_results["cumulative"][:max_indx + 1]
hist_df["cases"] = risk_results["cases"][:max_indx + 1]
hist_df["controls"] = risk_results["controls"][:max_indx + 1]
hist_df["total"] = hist_df["cases"] + hist_df["controls"]
hist_df.to_csv(options.stdout, sep="\t", index=None)
# 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("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("--method", dest="method", type="choice",
choices=["cases_explained", "probability_phenotype"],
help="Which results to report, either the proportion "
"of cases explained or the probability of the "
"phenotype given the number of alleles carried")
parser.add_option("--map-file", dest="map_file", type="string",
help="plink .map file with SNP positions")
parser.add_option("--ped-file", dest="ped_file", type="string",
help="plink ped file with phenotype and "
"genotype data - A2 major allele coded")
parser.add_option("--gwas-file", dest="gwas", type="string",
help="gwas results file, assumes Plink "
"output format. Must contain SNP, BP, "
"OR column headers. Assumes results relate "
"to the A1 allele")
parser.add_option("--flip-alleles", dest="flip", action="store_true",
help="force alleles to flip if OR < 1")
parser.add_option("--plot-statistic", dest="plot_stat", type="choice",
choices=["frequency", "cumulative"],
help="plot either cases frequency or cumulative "
"frequency of cases")
parser.add_option("--plot-path", dest="plot_path", type="string",
help="save path for plot")
parser.add_option("--flag-explained-recessive", dest="explained",
action="store_true",
help="flag individuals explained by carriage of "
"2 risk alleles - NOT IMPLIMENTED")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# required files are .ped file, .map file and gwas results file
E.info("reading GWAS results file: %s" % options.gwas)
snp_df = pd.read_table(options.gwas, sep="\t", header=0,
index_col=None)
snp_list = snp_df["SNP"].values
# parse ped file
E.info("Reading ped file: %s" % options.ped_file)
ped_df = gwas.parsePed(options.ped_file,
compound_geno=True)
# parse map file and get SNP indices that correspond to
# ped file genotypes
E.info("Fetching SNPs from map file: %s" % options.map_file)
snp_index = gwas.getSNPs(options.map_file,
snp_list)
E.info("SNPs found: %i" % len(snp_index))
# extract SNPs and ORs as key, value pairs
or_dict = snp_df.loc[:, ["SNP", "OR"]].to_dict(orient='list')
snp_or = dict(zip(or_dict["SNP"], or_dict["OR"]))
if options.flip:
E.info("Flipping major alleles to risk alleles")
flipped_genos = gwas.flipRiskAlleles(snp_index=snp_index,
snp_results=snp_or,
genos=ped_df["GENOS"].tolist())
# merge flipped genotypes with pedigree frame to get phenotypes
geno_df = pd.DataFrame(flipped_genos, index=ped_df["FID"])
else:
# split genos into a dataframe
genos = np.array(ped_df["GENOS"].tolist())
geno_df = pd.DataFrame(genos, index=ped_df["FID"])
merged = pd.merge(geno_df, ped_df, left_index=True, right_on="FID")
# need to discount missing genotypes > 1%
# frequencies of number of risk alleles by trait frequency
E.info("count #risk alleles per individual")
risk_results = gwas.countRiskAlleles(ped_frame=merged,
snp_index=snp_index.values(),
report=options.method,
flag=options.explained)
risk_freqs = risk_results["freqs"]
cumulative = risk_results["cumulative"]
# select results upto and including cumulative freq = 1.0
max_indx = [fx for fx, fy in enumerate(cumulative) if fy == 1.0][0]
max_freqs = risk_freqs[:max_indx + 1]
max_cum = cumulative[:max_indx + 1]
bins = [ix for ix, iy in enumerate(cumulative)][:max_indx + 1]
# plot!
if options.plot_stat == "frequency":
E.info("Generating plot of #risk alleles vs. P(Phenotype)")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_freqs,
savepath=options.plot_path,
ytitle="P(Phenotype)")
elif options.plot_stat == "cumulative":
E.info("Generating plot of #risk alleles vs. cumulative frequency")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_cum,
savepath=options.plot_path,
ytitle="Cumulative frequency cases")
hist_df["freq"] = risk_results["freqs"][:max_indx + 1]
hist_df["cumulative"] = risk_results["cumulative"][:max_indx + 1]
hist_df["cases"] = risk_results["cases"][:max_indx + 1]
hist_df["controls"] = risk_results["controls"][:max_indx + 1]
hist_df["total"] = hist_df["cases"] + hist_df["controls"]
hist_df.to_csv(options.stdout, sep="\t", index=None)
# write footer and output benchmark information.
E.Stop()
