def test_makes_df(self):
from idiva.clf.df import v0_df
from idiva.io import ReadVCF
from idiva.utils import seek_then_rewind
for k in PATHS:
with PATHS[k].open(mode='r') as fd:
assert isinstance(fd, io.TextIOBase)
with seek_then_rewind(fd):
datalines = list(ReadVCF(fd))
with seek_then_rewind(fd):
df = v0_df(ReadVCF(fd))
self.assertEqual(len(datalines), len(df))
def test_reads_all_lines(self):
for k in PATHS:
with open_maybe_gz(PATHS[k], mode='r') as fd:
vcf = ReadVCF(fd)
from idiva.utils import seek_then_rewind
with seek_then_rewind(fd, seek=None):
reference = len(fd.readlines())
with vcf.rewind_when_done:
candidate = len(list(vcf))
self.assertEqual(candidate, reference)
def check_all(fd: typing.TextIO):
from idiva.utils import seek_then_rewind
try:
with seek_then_rewind(fd):
bag_of_assumptions.alt_column(fd)
except (AssertionError, RuntimeError):
yield {'alt_column': False}
else:
yield {'alt_column': True}
try:
with seek_then_rewind(fd):
bag_of_assumptions.id_is_unique(fd)
except (AssertionError, RuntimeError):
yield {'The ID column is_unique': False}
else:
yield {'The ID column is_unique': True}
try:
with seek_then_rewind(fd):
bag_of_assumptions.samples_column(fd)
except (AssertionError, RuntimeError):
yield {'samples_column': False}
else:
yield {'samples_column': True}
try:
with seek_then_rewind(fd):
bag_of_assumptions.ref_column(fd)
except (AssertionError, RuntimeError):
yield {'ref_column': False}
else:
yield {'ref_column': True}
try:
with seek_then_rewind(fd):
bag_of_assumptions.format_is_gt(fd)
except (AssertionError, RuntimeError):
yield {'format_is_gt': False}
else:
yield {'format_is_gt': True}
def test_md5_head(self):
from idiva.io import ReadVCF, open_maybe_gz
from idiva.utils import seek_then_rewind
for (k, file) in PATHS_LARGE_HEAD.items():
with open_maybe_gz(file, mode='r') as fd:
assert isinstance(fd, io.TextIOBase)
with seek_then_rewind(fd, seek=0):
import hashlib
reference = hashlib.md5(fd.read().encode()).hexdigest()
candidate = ReadVCF(fd).md5
self.assertEqual(candidate, reference)
def head(src: io.TextIOBase, n=100) -> io.TextIOBase:
from idiva.utils import seek_then_rewind
from tempfile import NamedTemporaryFile
with NamedTemporaryFile(mode='w') as tf:
with seek_then_rewind(src):
# Write meta and header
line = "##"
while line.startswith("##"):
line = src.readline().strip()
print(line, file=tf)
assert line.startswith("#")
# Write a few datalines
for _ in range(n):
line = src.readline().strip()
assert not line.startswith("#")
print(line, file=tf, flush=True)
with open(tf.name, mode='r') as fd:
yield fd
def open_maybe_gz(file,
*,
mode='r') -> typing.Union[io.TextIOBase, io.BytesIO]:
"""
Open `file` for reading that could be a
- file descriptor
- path to file
- path to gzipped file
`mode` is either 'r' or 'rb', and has to be specified.
Usage:
with open_maybe_gz(path_to_file, mode='r') as fd:
print(fd.read())
"""
assert mode in ['r', 'rb']
if isinstance(file, io.TextIOBase):
assert (mode == 'r'), "Can't convert TextIOBase to mode='rb'."
yield file
return
if isinstance(file, io.IOBase):
assert isinstance(file, (io.BufferedIOBase, io.TextIOBase))
with contextlib.ExitStack() as stack:
import gzip
try:
with seek_then_rewind(file, seek=0):
with gzip.open(file) as test:
test.read(2)
except gzip.BadGzipFile:
# Assume this is not a gzipped stream
pass
else:
# gzip didn't complain
# interpret this as a gzipped stream
file = stack.enter_context(gzip.open(file))
if (mode == 'r'):
if not isinstance(file, io.TextIOBase):
assert isinstance(file, io.BufferedIOBase)
file = stack.enter_context(io.TextIOWrapper(file))
yield file
return
if isinstance(file, str) and re.match(r"^(http|https|ftp)://", file):
from idiva.download import download
with download(file).now.open(mode='rb') as fd:
with open_maybe_gz(fd, mode=mode) as fd:
yield fd
return
from pathlib import Path
assert Path(file).is_file()
file = str(file)
if file.endswith(".gz"):
import gzip
with gzip.open(file, mode='rb') as fd:
if (mode == 'r'):
yield io.TextIOWrapper(fd)
elif (mode == 'rb'):
yield fd
else:
with open(file, mode=mode) as fd:
yield fd
def rewind_when_done(self):
from idiva.utils import seek_then_rewind
with seek_then_rewind(self.fd, seek=None):
yield
def preload_all(self):
from idiva.utils import seek_then_rewind
with seek_then_rewind(self.fd, seek=None):
self.datalines = list(iter(self.datalines))
return self
def feature_extraction_batch(self, reader_ctrl: ReadVCF, reader_case: ReadVCF, names_ctrl: List[str],
names_case: List[str], batches_ctrl: List[int], batches_case: List[int], idx: int,
clf, id: List[int]):
"""
Returns a trained classifier on one batch
loads from both files some patients (one batch) for training
"""
with seek_then_rewind(reader_ctrl.fd, seek=reader_ctrl.dataline_start_pos) as fd_ctrl:
with seek_then_rewind(reader_case.fd, seek=reader_case.dataline_start_pos) as fd_case:
batch_names_ctrl = names_ctrl[:3]
batch_names_case = names_case[:3]
batch_names_ctrl.extend(names_ctrl[batches_ctrl[idx] + 3:batches_ctrl[idx + 1] + 3])
batch_names_case.extend(names_case[batches_case[idx] + 3:batches_case[idx + 1] + 3])
batch_columns_ctrl = [0, 1, 4]
batch_columns_case = [0, 1, 4]
batch_columns_ctrl.extend(list(range(batches_ctrl[idx] + 9, batches_ctrl[idx + 1] + 9)))
batch_columns_case.extend(list(range(batches_case[idx] + 9, batches_case[idx + 1] + 9)))
converter_dict_ctrl = {}
for column in batch_names_ctrl:
if column not in ['CHROM', 'POS', 'ALT']:
converter_dict_ctrl[column] = self.convert_strang
converter_dict_case = {}
for column in batch_names_case:
if column not in ['CHROM', 'POS', 'ALT']:
converter_dict_case[column] = self.convert_strang
dataframe_ctrl = pd.read_csv(fd_ctrl, sep='\t', header=None,
usecols=batch_columns_ctrl,
names=batch_names_ctrl, converters=converter_dict_ctrl)
dataframe_ctrl = dataframe_ctrl.drop_duplicates(['CHROM', 'POS', 'ALT'], keep='first')
dataframe_ctrl['ID'] = dataframe_ctrl[['CHROM', 'POS', 'ALT']].apply(index_map, axis=1)
dataframe_ctrl = dataframe_ctrl.drop(['CHROM', 'POS', 'ALT'], axis=1)
dataframe_ctrl = dataframe_ctrl.set_index('ID')
dataframe_ctrl = dataframe_ctrl.transpose()
dataframe_ctrl = dataframe_ctrl.reindex(columns=id, fill_value=4)
dataframe_case = pd.read_csv(fd_case, sep='\t', header=None,
usecols=batch_columns_case,
names=batch_names_case, converters=converter_dict_case)
dataframe_case = dataframe_case.drop_duplicates(['CHROM', 'POS', 'ALT'], keep='first')
dataframe_case['ID'] = dataframe_case[['CHROM', 'POS', 'ALT']].apply(index_map, axis=1)
dataframe_case = dataframe_case.drop(['CHROM', 'POS', 'ALT'], axis=1)
dataframe_case = dataframe_case.set_index('ID')
dataframe_case = dataframe_case.transpose()
dataframe_case = dataframe_case.reindex(columns=id, fill_value=4)
labels = np.zeros(dataframe_ctrl.shape[0])
labels = np.append(labels, np.ones(dataframe_case.shape[0]))
dataframe = dataframe_ctrl.append(dataframe_case)
dataframe, labels = shuffle(dataframe, labels, random_state=0)
"""
# for Random Forest Classifier
clf.n_estimators += 10000
clf.fit(dataframe, labels)
"""
clf.partial_fit(dataframe, labels, classes=[0, 1])
return clf
loaded_model = pickle.load(open(filename, 'rb'))
else:
warnings.warn("no model saved")
loaded_model = DummyClassifier()
return loaded_model
def align(case: ReadVCF, ctrl: ReadVCF):
"""
aligning case and control vcf file by joining on chrom, pos, ref and alt
"""
from idiva.utils import seek_then_rewind
dfs = {}
for (k, vcf) in zip(['case', 'ctrl'], [case, ctrl]):
with seek_then_rewind(vcf.fd, seek=vcf.dataline_start_pos) as fd:
dfs[k] = pd.read_csv(fd, sep='\t', usecols=[0, 1, 2, 3, 4], header=None,
names=["CHROM", "POS", "ID", "REF", "ALT"])
dfs[k].index = dfs[k].index.rename(name="rowid")
dfs[k] = dfs[k].reset_index().astype({'rowid': 'Int64'})
dfs['case'] = dfs['case'].drop_duplicates(['CHROM', 'POS', 'REF', 'ALT'], keep='first')
dfs['ctrl'] = dfs['ctrl'].drop_duplicates(['CHROM', 'POS', 'REF', 'ALT'], keep='first')
df = join(case=dfs['case'], ctrl=dfs['ctrl'])
df['CHROM'] = pd.to_numeric(df[['CHROM']].apply(translate_chrom, axis=1))
df['CPA_ID'] = df[['CHROM', 'POS', 'ALT']].apply(index_map, axis=1)
df = df.set_index('CPA_ID')
def translate_vcf(self, vcf) -> pd.DataFrame:
"""
Returns a dataframe that contains the following features from a vcf file
CHROM, POS, ID, VAR
"""
cache = (Path(__file__).parent.parent.parent.parent /
"input/download_cache").resolve()
assert cache.is_dir()
with ReadVCF.open(vcf) as reader:
with seek_then_rewind(reader.fd,
seek=reader.dataline_start_pos) as fd:
dataframe = pd.read_csv(fd,
sep='\t',
usecols=range(
len(DataHandler.INIT_COLS)),
header=None,
names=DataHandler.INIT_COLS,
dtype={
'CHROM': np.int,
'POS': np.int,
'ID': np.str,
'REF': np.str,
'ALT': np.str
})
# Check if ALT contains only one value or several values seperated by ','
assert (len([
uni for uni in dataframe['ALT'].unique().tolist()
if ',' in uni
]) == 0)
# store only SNP variants
dataframe = dataframe[dataframe['REF'].apply(
lambda x: {x}.issubset({'A', 'C', 'G', 'T'}))]
dataframe = dataframe[dataframe['ALT'].apply(
lambda x: {x}.issubset({'A', 'C', 'G', 'T'}))]
# Check if only SNP
for ref in dataframe['REF']:
assert (len(ref) == 1)
for alt in dataframe['ALT']:
assert (len(alt) == 1)
assert (set(dataframe['REF'].unique().tolist()).issubset(
{'A', 'C', 'G', 'T'}))
assert (set(dataframe['ALT'].unique().tolist()).issubset(
{'A', 'C', 'G', 'T'}))
dataframe['CHROM'] = pd.to_numeric(dataframe[['CHROM']].apply(
self.translate_chrom, axis=1))
dataframe = self.encode_ref_alt(dataframe)
dataframe.drop_duplicates()
# TODO: same CHROM POS and rsID but not same REF & ALT
# consequence of real world data (Kjong Nov 30)
# => identify samples by CHROM, POS and VAR
# same CHROM rsID REF ALT but not same POS
# => rsIDs are not completely unique !
# Ignore rsID (Kjong Nov 23)
"""
print(len(dataframe['ID'].unique().tolist()))
print(len(dataframe['ID'].tolist()))
CHROM POS ID REF ALT VAR
56638 17 1649616 rs544719440 A G 2
576511 17 19159733 rs540831825 A G 2
717227 17 27196477 rs202111951 T C 10
919995 17 34642425 rs568794696 C A 3
2105598 17 77663493 rs148485780 C T 5
CHROM POS ID REF ALT VAR
56637 17 1649616 rs544719440 A C 1
576510 17 19159733 rs540831825 A C 1
717226 17 27196477 rs202111951 T A 9
919587 17 34540858 rs568794696 C A 3
2105592 17 77663435 rs148485780 C T 5
"""
return dataframe
