def export_ldscore(ht, pop):
hm3_snps = hl.read_table(get_hm3_snplist_path(pop))
ht = ht.select(CHR=ht.locus.contig,
SNP=hl.variant_str(ht.locus, ht.alleles),
RSID=ht.rsid,
BP=ht.locus.position,
L2=ht.ld_score,
MAF=0.5 - hl.abs(0.5 - ht.AF))
count = ht.aggregate(
hl.struct(M=hl.agg.count(), M_5_50=hl.agg.sum(ht.MAF > 0.05)))
ht = ht.filter(hl.is_defined(hm3_snps[ht.locus, ht.alleles]))
ht = ht.key_by().drop('locus', 'alleles', 'MAF')
with hadoop_open(get_ld_score_flat_file_path(pop, extension='M'),
'w') as f:
f.write(f'{count.M}\n')
with hadoop_open(get_ld_score_flat_file_path(pop, extension='M_5_50'),
'w') as f:
f.write(f'{count.M_5_50}\n')
# LD score with variant ids
ht.drop('RSID').export(get_ld_score_flat_file_path(pop))
# with rsids
ht.transmute(SNP=ht.RSID).export(
get_ld_score_flat_file_path(pop, rsid=True))
def load_bin(ns, path):
m = np.zeros((ns, ns))
with utils.hadoop_open(path, 'rb') as f:
for i in range(ns):
for j in range(i + 1):
b = f.read(4)
self.assertEqual(len(b), 4)
m[i, j] = unpack('<f', bytearray(b))[0]
left = f.read()
self.assertEqual(len(left), 0)
return m
def test_hadoop_is_file(self, prefix: Optional[str] = None):
if prefix is None:
prefix = self.remote_tmpdir
a_file = f'{prefix}/test_hadoop_is_file.txt'
with hadoop_open(a_file, 'w') as f:
f.write("HELLO WORLD")
self.assertTrue(hl.hadoop_is_file(a_file))
self.assertFalse(hl.hadoop_is_file(f'{prefix}/'))
self.assertFalse(hl.hadoop_is_file(f'{prefix}/invalid-path'))
def test_hadoop_exists(self, prefix: Optional[str] = None):
if prefix is None:
prefix = self.remote_tmpdir
with hadoop_open(f'{prefix}/test_exists.txt', 'w') as f:
f.write("HELLO WORLD")
r_exists = f'{prefix}/test_exists.txt'
r_not_exists = f'{prefix}/not_exists.txt'
self.assertTrue(hl.hadoop_exists(r_exists))
self.assertFalse(hl.hadoop_exists(r_not_exists))
def test_hadoop_exists(self, bucket=None):
if bucket is None:
bucket = self.remote_bucket
with hadoop_open(f'{bucket}/test_exists.txt', 'w') as f:
f.write("HELLO WORLD")
r_exists = f'{bucket}/test_exists.txt'
r_not_exists = f'{bucket}/not_exists.txt'
self.assertTrue(hl.hadoop_exists(r_exists))
self.assertFalse(hl.hadoop_exists(r_not_exists))
def test_hadoop_is_file(self, bucket=None):
if bucket is None:
bucket = self.remote_bucket
a_file = f'{bucket}/test_hadoop_is_file.txt'
with hadoop_open(a_file, 'w') as f:
f.write("HELLO WORLD")
self.assertTrue(hl.hadoop_is_file(a_file))
self.assertFalse(hl.hadoop_is_file(f'{bucket}/'))
self.assertFalse(hl.hadoop_is_file(f'{bucket}/invalid-path'))
def load_grm(ns, nv, path):
m = np.zeros((ns, ns))
with utils.hadoop_open(path) as f:
i = 0
for l in f:
row = l.strip().split('\t')
self.assertEqual(int(row[2]), nv)
m[int(row[0]) - 1, int(row[1]) - 1] = float(row[3])
i += 1
self.assertEqual(i, ns * (ns + 1) / 2)
return m
def test_hadoop_methods(self, bucket=None):
if bucket is None:
bucket = self.remote_bucket
data = ['foo', 'bar', 'baz']
data.extend(map(str, range(100)))
with hadoop_open(f'{bucket}/test_out.txt', 'w') as f:
for d in data:
f.write(d)
f.write('\n')
with hadoop_open(f'{bucket}/test_out.txt') as f:
data2 = [line.strip() for line in f]
self.assertEqual(data, data2)
with hadoop_open(f'{bucket}/test_out.txt.gz', 'w') as f:
for d in data:
f.write(d)
f.write('\n')
with hadoop_open(f'{bucket}/test_out.txt.gz') as f:
data3 = [line.strip() for line in f]
self.assertEqual(data, data3)
hadoop_copy(f'{bucket}/test_out.txt.gz',
f'{bucket}/test_out.copy.txt.gz')
with hadoop_open(f'{bucket}/test_out.copy.txt.gz') as f:
data4 = [line.strip() for line in f]
self.assertEqual(data, data4)
local_fs = HadoopFS()
with local_fs.open(resource('randomBytes'), buffer_size=100) as f:
with hadoop_open(f'{bucket}/randomBytesOut',
'w',
buffer_size=2**18) as out:
b = f.read()
out.write(b)
with hadoop_open(f'{bucket}/randomBytesOut', buffer_size=2**18) as f:
b2 = f.read()
self.assertEqual(b, b2)
def test_hadoop_methods(self, prefix: Optional[str] = None):
if prefix is None:
prefix = self.remote_tmpdir
data = ['foo', 'bar', 'baz']
data.extend(map(str, range(100)))
with hadoop_open(f'{prefix}/test_out.txt', 'w') as f:
for d in data:
f.write(d)
f.write('\n')
with hadoop_open(f'{prefix}/test_out.txt') as f:
data2 = [line.strip() for line in f]
self.assertEqual(data, data2)
with hadoop_open(f'{prefix}/test_out.txt.gz', 'w') as f:
for d in data:
f.write(d)
f.write('\n')
with hadoop_open(f'{prefix}/test_out.txt.gz') as f:
data3 = [line.strip() for line in f]
self.assertEqual(data, data3)
hadoop_copy(f'{prefix}/test_out.txt.gz',
f'{prefix}/test_out.copy.txt.gz')
with hadoop_open(f'{prefix}/test_out.copy.txt.gz') as f:
data4 = [line.strip() for line in f]
self.assertEqual(data, data4)
local_fs = LocalFS()
with local_fs.open(os.path.join(self.local_dir, 'randomBytes'), 'rb', buffer_size=100) as f:
with hadoop_open(f'{prefix}/randomBytesOut', 'wb', buffer_size=2**18) as out:
b = f.read()
out.write(b)
with hadoop_open(f'{prefix}/randomBytesOut', 'rb', buffer_size=2**18) as f:
b2 = f.read()
self.assertEqual(b, b2)
def test_hadoop_stat(self, prefix: Optional[str] = None):
if prefix is None:
prefix = self.remote_tmpdir
data = ['foo', 'bar', 'baz']
data.extend(map(str, range(100)))
with hadoop_open(f'{prefix}/test_hadoop_stat.txt.gz', 'w') as f:
for d in data:
f.write(d)
f.write('\n')
stat1 = hl.hadoop_stat(f'{prefix}')
self.assertEqual(stat1['is_dir'], True)
hadoop_copy(f'{prefix}/test_hadoop_stat.txt.gz',
f'{prefix}/test_hadoop_stat.copy.txt.gz')
stat2 = hl.hadoop_stat(f'{prefix}/test_hadoop_stat.copy.txt.gz')
# The gzip format permits metadata which makes the compressed file's size unpredictable. In
# practice, Hadoop creates a 175 byte file and gzip.GzipFile creates a 202 byte file. The 27
# extra bytes appear to include at least the filename (20 bytes) and a modification timestamp.
assert stat2['size_bytes'] == 175 or stat2['size_bytes'] == 202
self.assertEqual(stat2['is_dir'], False)
self.assertTrue('path' in stat2)
def main(args):
hl.init(log='/assign_phecodes.log')
# Read in the Phecode (v1.2b1) <-> ICD 9/10 codes mapping
with hadoop_open(
'gs://ukb-diverse-pops/phecode/UKB_Phecode_v1.2b1_ICD_Mapping.txt',
'r') as f:
df = pd.read_csv(f, delimiter='\t', dtype=str)
list_of_icd_codes_to_include = [
row.icd_codes.split(',') for _, row in df.iterrows()
]
list_of_phecodes_to_exclude = [
row.exclude_phecodes.split(',') for _, row in df.iterrows()
]
df['icd_codes'] = list_of_icd_codes_to_include
df['exclude_phecodes'] = list_of_phecodes_to_exclude
# Convert it to HailTable
phecode_ht = hl.Table.from_pandas(df)
phecode_ht = phecode_ht.key_by('icd_codes')
phecode_ht = phecode_ht.checkpoint(
'gs://ukb-diverse-pops/phecode/UKB_Phecode_v1.2b1_ICD_Mapping.ht',
overwrite=args.overwrite)
# Retreive UKB ICD MatrixTable and combine codes based on Phecode definitions
icd_all = hl.read_matrix_table(get_ukb_pheno_mt_path('icd_all'))
mt = combine_phenotypes(icd_all,
icd_all.icd_code,
icd_all.any_codes,
list_of_icd_codes_to_include,
new_col_name='icd_codes',
new_entry_name='include_to_cases')
mt = mt.annotate_cols(
phecode=phecode_ht[mt.icd_codes].phecode,
phecode_sex=phecode_ht[mt.icd_codes].sex,
phecode_description=phecode_ht[mt.icd_codes].description,
phecode_group=phecode_ht[mt.icd_codes].group,
exclude_phecodes=phecode_ht[mt.icd_codes].exclude_phecodes)
# Annotate sex for sex-specific phenotypes
ukb_pheno_ht = hl.read_table(get_ukb_pheno_ht_path())
mt = mt.annotate_rows(isFemale=ukb_pheno_ht[mt.userId].sex == 0)
mt = checkpoint_tmp(mt)
# Compute phecode excluded from controls
mt = mt.key_cols_by()
exclude_mt = combine_phenotypes(mt,
mt.phecode,
mt.include_to_cases,
list_of_phecodes_to_exclude,
new_entry_name='exclude_from_controls')
exclude_mt = checkpoint_tmp(exclude_mt)
# Annotate exclusion
mt = mt.key_cols_by('exclude_phecodes')
mt = mt.annotate_entries(
exclude_sex=(hl.switch(mt.phecode_sex).when("males", mt.isFemale).when(
"females", ~mt.isFemale).default(False)),
exclude_from_controls=hl.coalesce(
exclude_mt[mt.userId, mt.exclude_phecodes].exclude_from_controls,
False))
# Compute final case/control status
# `case_control` becomes missing (NA) if a sample 1) is excluded because of sex, 2) is not cases and excluded from controls.
mt = mt.annotate_entries(case_control=hl.if_else(
mt.exclude_sex | (~mt.include_to_cases & mt.exclude_from_controls),
hl.null(hl.tbool), mt.include_to_cases))
mt = mt.key_cols_by('phecode')
mt.describe()
mt.write(get_ukb_pheno_mt_path('phecode'), overwrite=args.overwrite)
