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("--score-method",
dest="method",
type="choice",
choices=[
"PICS", "LDscore", "ABF", "R2_rank", "get_eigen",
"calc_prior", "credible_set", "summarise"
],
help="SNP scoring/prioritisation method to apply.")
parser.add_option("--database",
dest="database",
type="string",
help="SQL database containing LD information "
"in table format. Expects columns SNP_A, "
"SNP_B, R2, BP_A and BP_B (Plink --r2 output)")
parser.add_option("--ld-directory",
dest="ld_dir",
type="string",
help="directory containing tabix-index BGZIP "
"LD files. Assumes Plink used to calculate LD")
parser.add_option("--table-name",
dest="table",
type="string",
help="name of the SQL table containing the LD"
"values")
parser.add_option("--chromosome",
dest="chromosome",
type="string",
help="chromosome to subset the association results "
"file on")
parser.add_option("--ld-threshold",
dest="ld_threshold",
type="float",
help="the threshold of LD above which variants will "
"be taken forward.")
parser.add_option("--rank-threshold",
dest="rank_threshold",
type="float",
help="the threshold in terms of the top n% SNPs to "
"output based on the ranking metric. e.g. "
"--rank-threshold=0.01 is the top 1% SNPs")
parser.add_option("--credible-interval",
dest="interval",
type="float",
help="The credible set interval size to generate the "
"credible set of SNPs")
parser.add_option("--prior-variance",
dest="prior_var",
type="float",
help="the prior variance used to weight the SNP "
"variance")
parser.add_option("--fine-map-window",
dest="map_window",
type="int",
help="the region size to included around the index "
"SNP as the fine-mapping region.")
parser.add_option("--eigen-score-directory",
dest="eigen_dir",
type="string",
help="PATH to directory containing tabix indexed "
"eigen score files")
parser.add_option("--flat-prior",
dest="flat_prior",
action="store_true",
help="Ignore functional annotation information and "
"use an uninformative prior on each SNP")
parser.add_option("--snp-set",
dest="snp_set",
type="string",
help="Pre-defined SNP set as a list of SNP IDs."
"If used to calculate priors contains column of scores.")
parser.add_option(
"--distribution",
dest="dist",
type="choice",
choices=["normal", "t", "gamma", "lognormal", "exponential"],
help="distribution from which to draw prior "
"probabilities")
parser.add_option("--distribution-parameters",
dest="dist_params",
type="string",
help="distribution parameters as a comma-separated list")
parser.add_option("--lead-snp-id",
dest="lead_snp",
type="int",
help="0-based item number in filename")
parser.add_option("--filename-separator",
dest="separator",
type="string",
help="filename separator to extract information")
parser.add_option("--snp-column",
dest="snp_col",
type="int",
help="0-based index of SNP ID column number")
parser.add_option("--probability-column",
dest="prob_col",
type="int",
help="0-based index of posterior probabilities column"
" number")
parser.set_defaults(
ld_dir=None,
dist="normal",
dist_params=None,
snp_set=None,
prior_var=0.04,
interval=0.99,
eigen_dir=None,
map_window=100000,
ld_threshold=0.5,
database=None,
table=None,
flat_prior=False,
lead_snp=2,
separator="_",
snp_col=0,
prob_col=1,
)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
infile = argv[-1]
if len(infile.split(",")) > 1:
pass
else:
peek = pd.read_table(infile, nrows=5, sep="\s*", header=0)
try:
if len(peek["TEST"] != "ADD"):
clean = False
else:
clean = True
except KeyError:
clean = True
if options.method == "LDscore":
snpscores = gwas.snpPriorityScore(gwas_results=infile,
database=options.database,
table_name=options.table,
chromosome=options.chromosome,
ld_dir=options.ld_dir,
clean=clean)
# take top 1%, all SNPs doesn't achieve anything useful
ranks = int(len(snpscores.index) * 0.01)
snpscores = snpscores.iloc[:ranks]
elif options.method == "PICS":
snp_list = {}
if options.snp_set and not options.flat_prior:
with IOTools.openFile(options.snp_set, "r") as sfile:
for line in sfile.readlines():
snp = line.split("\t")[0]
try:
score = float(line.split("\t")[-1].rstrip("\n"))
except ValueError:
score = 0
snp_list[snp] = float(score)
# get the parameter estimates for the distribution
# if they have not been provided
if not options.dist_params:
dist_params = gwas.estimateDistributionParameters(
data=snp_list.values(), distribution=options.dist)
else:
dist_params = tuple(
[float(fx) for fx in options.dist_params.split(",")])
E.info("Calculating priors on SNPs")
priors = gwas.calcPriorsOnSnps(snp_list=snp_list,
distribution=options.dist,
params=dist_params)
elif options.snp_set and options.flat_prior:
with IOTools.openFile(options.snp_set, "r") as sfile:
for line in sfile.readlines():
snp = line.split("\t")[0]
snp_list[snp] = 1.0
priors = snp_list
else:
# allow for no priors or scores to be set,
# use of priors will be ignored,
# i.e. when prior and likelihood are not from
# conjugate distributions
priors = None
# PICS scores expects the gwas results file to
# only contain the region of interest, which
# represents an independent association signal
snpscores = gwas.PICSscore(gwas_results=infile,
database=options.database,
table_name=options.table,
chromosome=options.chromosome,
priors=priors,
clean=clean,
ld_dir=options.ld_dir,
ld_threshold=options.ld_threshold)
snpscores.columns = ["SNP", "PICS"]
posterior_sum = 0
snpscores.sort_values(ascending=False, inplace=True)
post_snps = []
for snp in snpscores.index:
if posterior_sum < 99.0:
posterior_sum += snpscores.loc[snp]
post_snps.append(snp)
else:
break
snpscores = snpscores.loc[post_snps]
snpscores.drop_duplicates(inplace=True)
elif options.method == "R2_rank":
# rank SNPs based on their LD with the lead
# SNP, take the top n% SNPs
snpscores = gwas.LdRank(gwas_results=infile,
database=options.database,
table_name=options.table,
ld_dir=options.ld_dir,
chromosome=options.chromosome,
ld_threshold=options.ld_threshold,
top_snps=options.rank_threshold,
clean=clean)
elif options.method == "ABF":
snpscores = gwas.ABFScore(gwas_results=infile,
region_size=options.map_window,
chromosome=options.chromosome,
prior_variance=options.prior_var,
clean=clean)
elif options.method == "get_eigen":
E.info("Fetching Eigen scores")
snpscores = gwas.getEigenScores(eigen_dir=options.eigen_dir,
bim_file=infile,
snp_file=options.snp_set)
snpscores = pd.DataFrame(snpscores).T
elif options.method == "credible_set":
E.info("Creating credible set")
snpscores = gwas.makeCredibleSet(probs_file=infile,
credible_set=options.interval,
lead_snp_indx=options.lead_snp,
filename_sep=options.separator,
snp_column=options.snp_col,
probs_column=options.prob_col)
elif options.method == "summarise":
E.info("Collating SNP prioritisation resuslts")
file_list = infile.split(",")
snpscores = gwas.summariseResults(file_list=file_list)
snpscores.to_csv(options.stdout, index_label="SNP", sep="\t")
# write footer and output benchmark information.
E.Stop()
