def run(args):
if os.path.exists(args.output):
logging.info("Output exists. Nope")
return
filters = {x[0]: x[1:] for x in args.filter}
maf_filter = float(filters["MAF"][0]) if "MAF" in filters else None
logging.info("Loading GTEX variant map")
gtex_snp_key = GTExMisc.load_gtex_variant_to_rsid(args.annotation[0])
logging.info("Processing genotype")
m = []
for mean, metadata, ids in ModelTraining.dosage_generator(
args.genotype,
gtex_snp_key,
dosage_conversion=ModelTraining._mean,
do_none=True):
if maf_filter:
f = mean / 2 if mean < 1 else 1 - mean / 2
if f < maf_filter:
continue
m.append(metadata)
m = Utilities.to_dataframe(m, [x[1] for x in Genotype.MetadataTFE.order])
if "TOP_CHR_POS_BY_FREQ" in filters:
logging.info("Simplifying multi-allelic variants")
m = Genotype._monoallelic_by_frequency(m)
logging.info("Saving...")
Utilities.save_dataframe(m, args.output)
logging.info("Finished")
def _process(d, key_to_snp, how="left"):
k = [(k_, key_to_snp[k_]) for k_ in d.variant_id if k_ in key_to_snp]
k = Utilities.to_dataframe(k, ["variant_id", "rsid"])
d = d.merge(k, on="variant_id", how=how)
d = d.rename(columns={"gene_id":"gene", "pval_nominal":"pvalue", "slope":"beta", "slope_se":"se"})
d = d[list(GTEx.GTExAllAssociations._fields)]
d = d.assign(maf = d.maf.astype(numpy.float32), beta = d.beta.astype(numpy.float32), se = d.se.astype(numpy.float32))
return d
def run(args):
logging.info("Starting process")
vf = pq.ParquetFile(args.parquet_genotype_metadata)
m = None
last_chromosome = None
r = []
for i, line in Utilities.iterate_file(args.regions):
if i == 0: continue
comps = line.strip().split()
count, m, last_chromosome = count_variants(comps[0], comps[1],
comps[2], vf, m,
last_chromosome, args)
r.append((comps[0], comps[1], comps[2], count))
r = Utilities.to_dataframe(r, ["chromosome", "start", "end", "count"])
Utilities.save_dataframe(r, args.output)
logging.info("Finished process")
