def test_extract_k1(self):
y = np.array([1, 3, 5, 8, 15])
ys = np.array([0, 0, 1, 1, 0])
x = np.array([2, 6, 10])
expect = np.array([[0, 0, 1, 1], [1, 1, 1, 2], [1, 0, 2, 5]])
result = fe.KnnCpgFeatureExtractor(1).extract(x, y, ys)
self._compare(result, expect)
x = np.array([0, 1, 3, 11, 15, 20])
expect = np.array([[np.nan, 0, np.nan, 1], [np.nan, 0, np.nan, 2],
[0, 1, 2, 2], [1, 0, 3, 4], [1, np.nan, 7, np.nan],
[0, np.nan, 5, np.nan]])
result = fe.KnnCpgFeatureExtractor(1).extract(x, y, ys)
self._compare(result, expect)
def test_extract_k3(self):
y = np.array([1, 3, 5, 8, 15])
ys = np.array([0, 0, 1, 1, 0])
x = np.array([2, 3, 6, 10, 15])
expect = np.array(
[[np.nan, np.nan, 0, 0, 1, 1, np.nan, np.nan, 1, 1, 3, 6],
[np.nan, np.nan, 0, 1, 1, 0, np.nan, np.nan, 2, 2, 5, 12],
[0, 0, 1, 1, 0, np.nan, 5, 3, 1, 2, 9, np.nan],
[0, 1, 1, 0, np.nan, np.nan, 7, 5, 2, 5, np.nan, np.nan],
[
0, 1, 1, np.nan, np.nan, np.nan, 12, 10, 7, np.nan, np.nan,
np.nan
]])
result = fe.KnnCpgFeatureExtractor(3).extract(x, y, ys)
self._compare(result, expect)
def test_larger_equal(self):
# y: 1 5 8 15
e = fe.KnnCpgFeatureExtractor()
f = e._KnnCpgFeatureExtractor__larger_equal
y = np.array([1, 5, 8, 15])
x = np.array([4, 6, 10])
expect = np.array([1, 2, 3])
result = f(x, y)
npt.assert_array_equal(result, expect)
x = np.array([-1, 0, 5, 14, 15, 16, 20])
expect = np.array([0, 0, 1, 3, 3, 4, 4])
result = f(x, y)
npt.assert_array_equal(result, expect)
def main(self, name, opts):
if opts.seed is not None:
np.random.seed(opts.seed)
logging.basicConfig(filename=opts.log_file,
format='%(levelname)s (%(asctime)s): %(message)s')
log = logging.getLogger(name)
if opts.verbose:
log.setLevel(logging.DEBUG)
else:
log.setLevel(logging.INFO)
log.debug(opts)
# Check input arguments
if not opts.cpg_profiles:
if not (opts.pos_file or opts.dna_files):
raise ValueError('Position table and DNA database expected!')
if opts.dna_wlen and opts.dna_wlen % 2 == 0:
raise '--dna_wlen must be odd!'
if opts.cpg_wlen and opts.cpg_wlen % 2 != 0:
raise '--cpg_wlen must be even!'
# Parse functions for computing output statistics
cpg_stats_meta = None
win_stats_meta = None
if opts.cpg_stats:
cpg_stats_meta = get_stats_meta(opts.cpg_stats)
if opts.win_stats:
win_stats_meta = get_stats_meta(opts.win_stats)
make_dir(opts.out_dir)
outputs = OrderedDict()
# Read single-cell profiles if provided
if opts.cpg_profiles:
log.info('Reading CpG profiles ...')
outputs['cpg'] = read_cpg_profiles(
opts.cpg_profiles,
chromos=opts.chromos,
nb_sample=opts.nb_sample,
nb_sample_chromo=opts.nb_sample_chromo,
log=log.info)
# Create table with unique positions
if opts.pos_file: #the pos_file provide the CpG positions which need to be predicted
# Read positions from file
log.info('Reading position table ...')
pos_table = pd.read_table(opts.pos_file,
usecols=[0, 1],
dtype={
0: str,
1: np.int32
},
header=None,
comment='#')
pos_table.columns = ['chromo', 'pos']
pos_table['chromo'] = dat.format_chromo(pos_table['chromo'])
pos_table = prepro_pos_table(pos_table)
else:
# Extract positions from profiles, if not provided. Predict position which available in at least one cells.
pos_tables = []
for cpg_table in list(outputs['cpg'].values()):
pos_tables.append(cpg_table[['chromo', 'pos']])
pos_table = prepro_pos_table(pos_tables)
if opts.chromos:
pos_table = pos_table.loc[pos_table.chromo.isin(opts.chromos)]
if opts.nb_sample_chromo:
pos_table = dat.sample_from_chromo(pos_table,
opts.nb_sample_chromo)
if opts.nb_sample:
pos_table = pos_table.iloc[:opts.nb_sample]
log.info('%d samples' % len(pos_table))
make_dir(opts.out_dir)
# Iterate over chromosomes
# ------------------------
for chromo in pos_table.chromo.unique():
log.info('-' * 80)
log.info('Chromosome %s ...' % (chromo))
idx = pos_table.chromo == chromo ##idx is T/F for whether the entries are equal to the chromo
chromo_pos = pos_table.loc[
idx].pos.values #a numpy array with 1D data
chromo_outputs = OrderedDict()
if 'cpg' in outputs:
# Concatenate CpG tables into single nb_site x nb_output matrix
chromo_outputs['cpg'] = map_cpg_tables(outputs['cpg'], chromo,
chromo_pos)
#chromo_outputs, one array called 'cpg', 'cpg' has #sample array,
#each item is mapped table of target_pos with value filled
#OrderedDict([('BS27_1_SER', array([1, 1, 1, ..., 1, 1, 0], dtype=int8)),
#('BS27_3_SER', array([-1, 1, 1, ..., 1, -1, -1], dtype=int8))])
chromo_outputs['cpg_mat'] = np.vstack(
list(chromo_outputs['cpg'].values())).T
#add one more array to it. np.vstack, stack array sequence vertically
#chromo_outputs['cpg_mat'].shape=(402166, 2)
#402166 is the CHR1 target pos number, 2 is the input two samples, BS27_1_SER, BS27_3_SER
assert len(chromo_outputs['cpg_mat']) == len(chromo_pos)
if 'cpg_mat' in chromo_outputs and opts.cpg_cov:
cov = np.sum(chromo_outputs['cpg_mat'] != dat.CPG_NAN, axis=1)
assert np.all(cov >= 1)
idx = cov >= opts.cpg_cov
tmp = '%s sites matched minimum coverage filter'
tmp %= format_out_of(idx.sum(), len(idx))
log.info(tmp)
if idx.sum() == 0:
continue
chromo_pos = chromo_pos[idx]
chromo_outputs = select_dict(chromo_outputs, idx)
# Read DNA of chromosome
chromo_dna = None
if opts.dna_files: #this will only read the corresponding chromosome sequence
chromo_dna = fasta.read_chromo(
opts.dna_files,
chromo) #chromo_dna is string, len=195471971 for chr1
annos = None
if opts.anno_files:
log.info('Annotating CpG sites ...')
annos = dict()
for anno_file in opts.anno_files:
name = split_ext(anno_file)
annos[name] = annotate(anno_file, chromo, chromo_pos)
# Iterate over chunks
# -------------------
nb_chunk = int(np.ceil(len(chromo_pos) / opts.chunk_size))
for chunk in range(nb_chunk):
log.info('Chunk \t%d / %d' % (chunk + 1, nb_chunk))
chunk_start = chunk * opts.chunk_size
chunk_end = min(len(chromo_pos), chunk_start + opts.chunk_size)
chunk_idx = slice(chunk_start, chunk_end)
chunk_pos = chromo_pos[chunk_idx]
chunk_outputs = select_dict(chromo_outputs,
chunk_idx) #OrderedDict()
#chunk_outputs is 1D array
filename = 'c%s_%06d-%06d.h5' % (chromo, chunk_start,
chunk_end)
filename = os.path.join(opts.out_dir, filename)
chunk_file = h5.File(filename, 'w')
# Write positions
chunk_file.create_dataset(
'chromo', shape=(len(chunk_pos), ),
dtype='S2') #create_dataset() in default for h5py
chunk_file['chromo'][:] = chromo.encode(
) #set the chunk_file['chromo'] = 1 for all.
#chunk_file['chromo'].shape = (32768,)
chunk_file.create_dataset('pos',
data=chunk_pos,
dtype=np.int32)
#chunk_file['pos'].shape = (32768,) # the size is default chunk_size
if len(chunk_outputs): #len(chunk_outputs)=2
out_group = chunk_file.create_group('outputs')
#for now, type(out_group) = <class 'h5py._hl.group.Group'>
#list(out_group) = []
# Write cpg profiles
if 'cpg' in chunk_outputs:
for name, value in six.iteritems(chunk_outputs['cpg']):
#name = ["BS27_1_SER", 'BS27_3_SER'] # the sample name
#value= 2 numpy array, both with shape=(32768,)
assert len(value) == len(chunk_pos)
# Round continuous values
out_group.create_dataset('cpg/%s' % name,
data=value.round(),
dtype=np.int8,
compression='gzip')
#type(out_group)= <class 'h5py._hl.group.Group'>
#list(out_group) = ['cpg']
#list(out_group['cpg']) = ['BS27_1_SER', 'BS27_3_SER']
# Compute and write statistics
if cpg_stats_meta is not None:
log.info('Computing per CpG statistics ...')
cpg_mat = np.ma.masked_values(chunk_outputs['cpg_mat'],
dat.CPG_NAN)
#cpg_mat.shape=(32768, 2)
mask = np.sum(~cpg_mat.mask, axis=1)
mask = mask < opts.cpg_stats_cov
for name, fun in six.iteritems(cpg_stats_meta):
stat = fun[0](cpg_mat).data.astype(fun[1])
stat[mask] = dat.CPG_NAN
assert len(stat) == len(chunk_pos)
out_group.create_dataset('cpg_stats/%s' % name,
data=stat,
dtype=fun[1],
compression='gzip')
#until here:
#>>> chunk_file.visit(printname)
#chromo
#outputs
#outputs/cpg
#outputs/cpg/BS27_1_SER
#utputs/cpg/BS27_3_SER
#pos
# Write input features
in_group = chunk_file.create_group('inputs')
# DNA windows
if chromo_dna:
log.info('Extracting DNA sequence windows ...')
dna_wins = extract_seq_windows(chromo_dna,
pos=chunk_pos,
wlen=opts.dna_wlen)
#give the fasta sequence of one chromosome ('chromo_dna'), and targeted position ('chunk_pos')
#, and wlen=1001, return a numpy array with shape as (32768, 1001). The array has been transfered as
#number rather than base pair
assert len(dna_wins) == len(chunk_pos)
in_group.create_dataset('dna',
data=dna_wins,
dtype=np.int8,
compression='gzip')
#>>> in_group.visit(printname) = dna
# CpG neighbors
if opts.cpg_wlen:
log.info('Extracting CpG neighbors ...')
cpg_ext = fext.KnnCpgFeatureExtractor(opts.cpg_wlen // 2)
context_group = in_group.create_group('cpg')
# outputs['cpg'], since neighboring CpG sites might lie
# outside chunk borders and un-mapped values are needed
for name, cpg_table in six.iteritems(outputs['cpg']):
#name="BS27_1_SER" and "BS27_3_SER"
#cpg_table = numpy array, with three columns information for each input sample.
cpg_table = cpg_table.loc[cpg_table.chromo == chromo]
state, dist = cpg_ext.extract(
chunk_pos, cpg_table.pos.values,
cpg_table.value.values
) #extract the cpg distance and state with wlen
nan = np.isnan(state)
state[
nan] = dat.CPG_NAN #set nan value as -1, which means unknown
dist[nan] = dat.CPG_NAN
# States can be binary (np.int8) or continuous
# (np.float32).
state = state.astype(cpg_table.value.dtype,
copy=False) #set data type
dist = dist.astype(np.float32, copy=False)
assert len(state) == len(chunk_pos)
assert len(dist) == len(chunk_pos)
assert np.all((dist > 0) | (dist == dat.CPG_NAN))
group = context_group.create_group(name)
group.create_dataset('state',
data=state,
compression='gzip')
group.create_dataset('dist',
data=dist,
compression='gzip')
#list(group) = ['state','dist']
if win_stats_meta is not None and opts.cpg_wlen:
log.info('Computing window-based statistics ...')
states = []
dists = []
cpg_states = []
cpg_group = out_group['cpg']
context_group = in_group['cpg']
for output_name in six.iterkeys(cpg_group):
state = context_group[output_name]['state'].value
states.append(np.expand_dims(state, 2))
dist = context_group[output_name]['dist'].value
dists.append(np.expand_dims(dist, 2))
cpg_states.append(cpg_group[output_name].value)
# samples x outputs x cpg_wlen
states = np.swapaxes(np.concatenate(states, axis=2), 1, 2)
dists = np.swapaxes(np.concatenate(dists, axis=2), 1, 2)
cpg_states = np.expand_dims(np.vstack(cpg_states).T, 2)
cpg_dists = np.zeros_like(cpg_states)
states = np.concatenate([states, cpg_states], axis=2)
dists = np.concatenate([dists, cpg_dists], axis=2)
for wlen in opts.win_stats_wlen:
idx = (states == dat.CPG_NAN) | (dists > wlen // 2)
states_wlen = np.ma.masked_array(states, idx)
group = out_group.create_group('win_stats/%d' % wlen)
for name, fun in six.iteritems(win_stats_meta):
stat = fun[0](states_wlen)
if hasattr(stat, 'mask'):
idx = stat.mask
stat = stat.data
if np.sum(idx):
stat[idx] = dat.CPG_NAN
group.create_dataset(name,
data=stat,
dtype=fun[1],
compression='gzip')
if annos:
log.info('Adding annotations ...')
group = in_group.create_group('annos')
for name, anno in six.iteritems(annos):
group.create_dataset(name,
data=anno[chunk_idx],
dtype='int8',
compression='gzip')
chunk_file.close()
log.info('Done!')
return 0
def main(self, name, opts):
logging.basicConfig(filename=opts.log_file,
format='%(levelname)s (%(asctime)s): %(message)s')
log = logging.getLogger(name)
if opts.verbose:
log.setLevel(logging.DEBUG)
else:
log.setLevel(logging.INFO)
log.debug(opts)
# Check input arguments
if not (opts.cpg_profiles or opts.bulk_profiles):
if not (opts.pos_file or opts.dna_files):
raise ValueError('Position table and DNA database expected!')
if opts.dna_wlen and opts.dna_wlen % 2 == 0:
raise '--dna_wlen must be odd!'
if opts.cpg_wlen and opts.cpg_wlen % 2 != 0:
raise '--cpg_wlen must be even!'
# Parse functions for computing output statistics
cpg_stats_meta = None
win_stats_meta = None
if opts.stats:
cpg_stats_meta = get_stats_meta(opts.stats)
if opts.win_stats:
win_stats_meta = get_stats_meta(opts.win_stats)
make_dir(opts.out_dir)
outputs = OrderedDict()
# Read single-cell profiles if provided
if opts.cpg_profiles:
log.info('Reading single-cell profiles ...')
outputs['cpg'] = read_cpg_profiles(opts.cpg_profiles,
chromos=opts.chromos,
nb_sample=opts.nb_sample)
if opts.bulk_profiles:
log.info('Reading bulk profiles ...')
outputs['bulk'] = read_cpg_profiles(opts.bulk_profiles,
chromos=opts.chromos,
nb_sample=opts.nb_sample,
round=False)
# Create table with unique positions
if opts.pos_file:
# Read positions from file
log.info('Reading position table ...')
pos_table = pd.read_table(opts.pos_file,
usecols=[0, 1],
dtype={
0: str,
1: np.int32
},
header=None,
comment='#')
pos_table.columns = ['chromo', 'pos']
pos_table['chromo'] = dat.format_chromo(pos_table['chromo'])
pos_table = prepro_pos_table(pos_table)
else:
# Extract positions from profiles
pos_tables = []
for cpg_table in list(outputs['cpg'].values()):
pos_tables.append(cpg_table[['chromo', 'pos']])
pos_table = prepro_pos_table(pos_tables)
if opts.chromos:
pos_table = pos_table.loc[pos_table.chromo.isin(opts.chromos)]
if opts.nb_sample:
pos_table = pos_table.iloc[:opts.nb_sample]
log.info('%d samples' % len(pos_table))
make_dir(opts.out_dir)
# Iterate over chromosomes
# ------------------------
for chromo in pos_table.chromo.unique():
log.info('-' * 80)
log.info('Chromosome %s ...' % (chromo))
idx = pos_table.chromo == chromo
chromo_pos = pos_table.loc[idx].pos.values
chromo_outputs = OrderedDict()
if 'cpg' in outputs:
# Concatenate CpG tables into single nb_site x nb_output matrix
chromo_outputs['cpg'] = map_cpg_tables(outputs['cpg'], chromo,
chromo_pos)
chromo_outputs['cpg_mat'] = np.vstack(
list(chromo_outputs['cpg'].values())).T
assert len(chromo_outputs['cpg_mat']) == len(chromo_pos)
if 'bulk' in outputs:
# Concatenate CpG tables into single nb_site x nb_output matrix
chromo_outputs['bulk'] = map_cpg_tables(
outputs['bulk'], chromo, chromo_pos)
if 'cpg_mat' in chromo_outputs and opts.cpg_cov:
cov = np.sum(chromo_outputs['cpg_mat'] != dat.CPG_NAN, axis=1)
assert np.all(cov >= 1)
idx = cov >= opts.cpg_cov
tmp = '%s sites matched minimum coverage filter'
tmp %= format_out_of(idx.sum(), len(idx))
log.info(tmp)
if idx.sum() == 0:
continue
chromo_pos = chromo_pos[idx]
chromo_outputs = select_dict(chromo_outputs, idx)
# Read DNA of chromosome
chromo_dna = None
if opts.dna_files:
chromo_dna = fasta.read_chromo(opts.dna_files, chromo)
annos = None
if opts.anno_files:
log.info('Annotating CpG sites ...')
annos = dict()
for anno_file in opts.anno_files:
name = split_ext(anno_file)
annos[name] = annotate(anno_file, chromo, chromo_pos)
# Iterate over chunks
# -------------------
nb_chunk = int(np.ceil(len(chromo_pos) / opts.chunk_size))
for chunk in range(nb_chunk):
log.info('Chunk \t%d / %d' % (chunk + 1, nb_chunk))
chunk_start = chunk * opts.chunk_size
chunk_end = min(len(chromo_pos), chunk_start + opts.chunk_size)
chunk_idx = slice(chunk_start, chunk_end)
chunk_pos = chromo_pos[chunk_idx]
chunk_outputs = select_dict(chromo_outputs, chunk_idx)
filename = 'c%s_%06d-%06d.h5' % (chromo, chunk_start,
chunk_end)
filename = os.path.join(opts.out_dir, filename)
chunk_file = h5.File(filename, 'w')
# Write positions
chunk_file.create_dataset('chromo',
shape=(len(chunk_pos), ),
dtype='S2')
chunk_file['chromo'][:] = chromo.encode()
chunk_file.create_dataset('pos',
data=chunk_pos,
dtype=np.int32)
if len(chunk_outputs):
out_group = chunk_file.create_group('outputs')
# Write cpg profiles
if 'cpg' in chunk_outputs:
for name, value in chunk_outputs['cpg'].items():
assert len(value) == len(chunk_pos)
out_group.create_dataset('cpg/%s' % name,
data=value,
dtype=np.int8,
compression='gzip')
# Compute and write statistics
if cpg_stats_meta is not None:
log.info('Computing per CpG statistics ...')
cpg_mat = np.ma.masked_values(chunk_outputs['cpg_mat'],
dat.CPG_NAN)
mask = np.sum(~cpg_mat.mask, axis=1)
mask = mask < opts.stats_cov
for name, fun in cpg_stats_meta.items():
stat = fun[0](cpg_mat).data.astype(fun[1])
stat[mask] = dat.CPG_NAN
assert len(stat) == len(chunk_pos)
out_group.create_dataset('stats/%s' % name,
data=stat,
dtype=fun[1],
compression='gzip')
# Write bulk profiles
if 'bulk' in chunk_outputs:
for name, value in chunk_outputs['bulk'].items():
assert len(value) == len(chunk_pos)
out_group.create_dataset('bulk/%s' % name,
data=value,
dtype=np.float32,
compression='gzip')
# Write input features
in_group = chunk_file.create_group('inputs')
# DNA windows
if chromo_dna:
log.info('Extracting DNA sequence windows ...')
dna_wins = extract_seq_windows(chromo_dna,
pos=chunk_pos,
wlen=opts.dna_wlen)
assert len(dna_wins) == len(chunk_pos)
in_group.create_dataset('dna',
data=dna_wins,
dtype=np.int8,
compression='gzip')
# CpG neighbors
if opts.cpg_wlen:
log.info('Extracting CpG neighbors ...')
cpg_ext = fext.KnnCpgFeatureExtractor(opts.cpg_wlen // 2)
context_group = in_group.create_group('cpg')
# outputs['cpg'], since neighboring CpG sites might lie
# outside chunk borders and un-mapped values are needed
for name, cpg_table in outputs['cpg'].items():
cpg_table = cpg_table.loc[cpg_table.chromo == chromo]
state, dist = cpg_ext.extract(chunk_pos,
cpg_table.pos.values,
cpg_table.value.values)
nan = np.isnan(state)
state[nan] = dat.CPG_NAN
dist[nan] = dat.CPG_NAN
state = state.astype(np.int8, copy=False)
dist = dist.astype(np.float32, copy=False)
assert len(state) == len(chunk_pos)
assert np.all((state == 0) | (state == 1)
| (state == dat.CPG_NAN))
assert len(dist) == len(chunk_pos)
assert np.all((dist > 0) | (dist == dat.CPG_NAN))
group = context_group.create_group(name)
group.create_dataset('state',
data=state,
compression='gzip')
group.create_dataset('dist',
data=dist,
compression='gzip')
if win_stats_meta is not None and opts.cpg_wlen:
log.info('Computing window-based statistics ...')
states = []
dists = []
cpg_states = []
cpg_group = out_group['cpg']
context_group = in_group['cpg']
for output_name in cpg_group.keys():
state = context_group[output_name]['state'].value
states.append(np.expand_dims(state, 2))
dist = context_group[output_name]['dist'].value
dists.append(np.expand_dims(dist, 2))
cpg_states.append(cpg_group[output_name].value)
# samples x outputs x cpg_wlen
states = np.swapaxes(np.concatenate(states, axis=2), 1, 2)
dists = np.swapaxes(np.concatenate(dists, axis=2), 1, 2)
cpg_states = np.expand_dims(np.vstack(cpg_states).T, 2)
cpg_dists = np.zeros_like(cpg_states)
states = np.concatenate([states, cpg_states], axis=2)
dists = np.concatenate([dists, cpg_dists], axis=2)
for wlen in opts.win_stats_wlen:
idx = (states == dat.CPG_NAN) | (dists > wlen // 2)
states_wlen = np.ma.masked_array(states, idx)
group = out_group.create_group('win_stats/%d' % wlen)
for name, fun in win_stats_meta.items():
stat = fun[0](states_wlen)
if hasattr(stat, 'mask'):
idx = stat.mask
stat = stat.data
if np.sum(idx):
stat[idx] = dat.CPG_NAN
group.create_dataset(name,
data=stat,
dtype=fun[1],
compression='gzip')
if annos:
log.info('Adding annotations ...')
group = in_group.create_group('annos')
for name, anno in annos.items():
group.create_dataset(name,
data=anno[chunk_idx],
dtype='int8',
compression='gzip')
chunk_file.close()
log.info('Done!')
return 0
def run_dcpg_data(pos_file = None,
cpg_profiles = None,
dna_files = None,
cpg_wlen=None,
cpg_cov = 1,
dna_wlen=1001,
anno_files=None,
chromos = None,
nb_sample = None,
nb_sample_chromo = None,
chunk_size = 32768,
seed = 0,
verbose = False):
if seed is not None:
np.random.seed(seed)
# FIXME
name = "dcpg_data"
logging.basicConfig(format='%(levelname)s (%(asctime)s): %(message)s')
log = logging.getLogger(name)
if verbose:
log.setLevel(logging.DEBUG)
else:
log.setLevel(logging.INFO)
# Check input arguments
if not cpg_profiles:
if not (pos_file or dna_files):
raise ValueError('Position table and DNA database expected!')
if dna_wlen and dna_wlen % 2 == 0:
raise 'dna_wlen must be odd!'
if cpg_wlen and cpg_wlen % 2 != 0:
raise 'cpg_wlen must be even!'
"""
# Parse functions for computing output statistics
cpg_stats_meta = None
win_stats_meta = None
if cpg_stats:
cpg_stats_meta = get_stats_meta(cpg_stats)
if win_stats:
win_stats_meta = get_stats_meta(win_stats)
"""
outputs = OrderedDict()
# Read single-cell profiles if provided
if cpg_profiles:
log.info('Reading CpG profiles ...')
outputs['cpg'] = read_cpg_profiles(
cpg_profiles,
chromos=chromos,
nb_sample=nb_sample,
nb_sample_chromo=nb_sample_chromo,
log=log.info)
# Create table with unique positions
if pos_file:
# Read positions from file
log.info('Reading position table ...')
pos_table = pd.read_table(pos_file, usecols=[0, 1],
dtype={0: str, 1: np.int32},
header=None, comment='#')
pos_table.columns = ['chromo', 'pos']
pos_table['chromo'] = dat.format_chromo(pos_table['chromo'])
pos_table = prepro_pos_table(pos_table)
else:
# Extract positions from profiles
pos_tables = []
for cpg_table in list(outputs['cpg'].values()):
pos_tables.append(cpg_table[['chromo', 'pos']])
pos_table = prepro_pos_table(pos_tables)
if chromos:
pos_table = pos_table.loc[pos_table.chromo.isin(chromos)]
if nb_sample_chromo:
pos_table = dat.sample_from_chromo(pos_table, nb_sample_chromo)
if nb_sample:
pos_table = pos_table.iloc[:nb_sample]
log.info('%d samples' % len(pos_table))
# Iterate over chromosomes
# ------------------------
for chromo in pos_table.chromo.unique():
log.info('-' * 80)
log.info('Chromosome %s ...' % (chromo))
idx = pos_table.chromo == chromo
chromo_pos = pos_table.loc[idx].pos.values
chromo_outputs = OrderedDict()
if 'cpg' in outputs:
# Concatenate CpG tables into single nb_site x nb_output matrix
chromo_outputs['cpg'] = map_cpg_tables(outputs['cpg'],
chromo, chromo_pos)
chromo_outputs['cpg_mat'] = np.vstack(
list(chromo_outputs['cpg'].values())).T
assert len(chromo_outputs['cpg_mat']) == len(chromo_pos)
if 'cpg_mat' in chromo_outputs and cpg_cov:
cov = np.sum(chromo_outputs['cpg_mat'] != dat.CPG_NAN, axis=1)
assert np.all(cov >= 1)
idx = cov >= cpg_cov
tmp = '%s sites matched minimum coverage filter'
tmp %= format_out_of(idx.sum(), len(idx))
log.info(tmp)
if idx.sum() == 0:
continue
chromo_pos = chromo_pos[idx]
chromo_outputs = select_dict(chromo_outputs, idx)
# Read DNA of chromosome
chromo_dna = None
if dna_files:
chromo_dna = fasta.read_chromo(dna_files, chromo)
annos = None
if anno_files:
log.info('Annotating CpG sites ...')
annos = dict()
for anno_file in anno_files:
name = split_ext(anno_file)
annos[name] = annotate(anno_file, chromo, chromo_pos)
# Iterate over chunks
# -------------------
nb_chunk = int(np.ceil(len(chromo_pos) / chunk_size))
for chunk in range(nb_chunk):
log.info('Chunk \t%d / %d' % (chunk + 1, nb_chunk))
chunk_start = chunk * chunk_size
chunk_end = min(len(chromo_pos), chunk_start + chunk_size)
chunk_idx = slice(chunk_start, chunk_end)
chunk_pos = chromo_pos[chunk_idx]
chunk_outputs = select_dict(chromo_outputs, chunk_idx)
#filename = 'c%s_%06d-%06d.h5' % (chromo, chunk_start, chunk_end)
#filename = os.path.join(out_dir, filename)
#chunk_file = h5.File(filename, 'w')
# Write positions
#chunk_file.create_dataset('chromo', shape=(len(chunk_pos),),
# dtype='S2')
#chunk_file['chromo'][:] = chromo.encode()
#chunk_file.create_dataset('pos', data=chunk_pos, dtype=np.int32)
yield_dict = {}
yield_dict["chromo"] = np.array([chromo.encode()]*len(chunk_pos), dtype='S2')
yield_dict["pos"] = np.array(chunk_pos, dtype=np.int32)
if len(chunk_outputs):
#out_group = chunk_file.create_group('outputs')
yield_dict["outputs"] = {}
out_group = yield_dict["outputs"]
# Write cpg profiles
if 'cpg' in chunk_outputs:
yield_dict["outputs"]['cpg']={}
for name, value in six.iteritems(chunk_outputs['cpg']):
assert len(value) == len(chunk_pos)
# Round continuous values
#out_group.create_dataset('cpg/%s' % name,
# data=value.round(),
# dtype=np.int8,
# compression='gzip')
out_group['cpg'][name] = np.array(value.round(), np.int8)
"""
# Compute and write statistics
if cpg_stats_meta is not None:
log.info('Computing per CpG statistics ...')
cpg_mat = np.ma.masked_values(chunk_outputs['cpg_mat'],
dat.CPG_NAN)
mask = np.sum(~cpg_mat.mask, axis=1)
mask = mask < cpg_stats_cov
for name, fun in six.iteritems(cpg_stats_meta):
stat = fun[0](cpg_mat).data.astype(fun[1])
stat[mask] = dat.CPG_NAN
assert len(stat) == len(chunk_pos)
out_group.create_dataset('cpg_stats/%s' % name,
data=stat,
dtype=fun[1],
compression='gzip')
"""
# Write input features
#in_group = chunk_file.create_group('inputs')
yield_dict["inputs"] = {}
in_group = yield_dict["inputs"]
# DNA windows
if chromo_dna:
log.info('Extracting DNA sequence windows ...')
dna_wins = extract_seq_windows(chromo_dna, pos=chunk_pos,
wlen=dna_wlen)
assert len(dna_wins) == len(chunk_pos)
#in_group.create_dataset('dna', data=dna_wins, dtype=np.int8,
# compression='gzip')
in_group['dna'] = np.array(dna_wins, dtype=np.int8)
# CpG neighbors
if cpg_wlen:
log.info('Extracting CpG neighbors ...')
cpg_ext = fext.KnnCpgFeatureExtractor(cpg_wlen // 2)
#context_group = in_group.create_group('cpg')
in_group['cpg'] = {}
context_group = in_group['cpg']
# outputs['cpg'], since neighboring CpG sites might lie
# outside chunk borders and un-mapped values are needed
for name, cpg_table in six.iteritems(outputs['cpg']):
cpg_table = cpg_table.loc[cpg_table.chromo == chromo]
state, dist = cpg_ext.extract(chunk_pos,
cpg_table.pos.values,
cpg_table.value.values)
nan = np.isnan(state)
state[nan] = dat.CPG_NAN
dist[nan] = dat.CPG_NAN
# States can be binary (np.int8) or continuous
# (np.float32).
state = state.astype(cpg_table.value.dtype, copy=False)
dist = dist.astype(np.float32, copy=False)
assert len(state) == len(chunk_pos)
assert len(dist) == len(chunk_pos)
assert np.all((dist > 0) | (dist == dat.CPG_NAN))
#group = context_group.create_group(name)
#group.create_dataset('state', data=state,
# compression='gzip')
#group.create_dataset('dist', data=dist,
# compression='gzip')
context_group[name] = {'state': state, 'dist':dist}
"""
if win_stats_meta is not None and cpg_wlen:
log.info('Computing window-based statistics ...')
states = []
dists = []
cpg_states = []
cpg_group = out_group['cpg']
context_group = in_group['cpg']
for output_name in six.iterkeys(cpg_group):
state = context_group[output_name]['state']#.value
states.append(np.expand_dims(state, 2))
dist = context_group[output_name]['dist']#.value
dists.append(np.expand_dims(dist, 2))
#cpg_states.append(cpg_group[output_name].value)
cpg_states.append(cpg_group[output_name])
# samples x outputs x cpg_wlen
states = np.swapaxes(np.concatenate(states, axis=2), 1, 2)
dists = np.swapaxes(np.concatenate(dists, axis=2), 1, 2)
cpg_states = np.expand_dims(np.vstack(cpg_states).T, 2)
cpg_dists = np.zeros_like(cpg_states)
states = np.concatenate([states, cpg_states], axis=2)
dists = np.concatenate([dists, cpg_dists], axis=2)
for wlen in win_stats_wlen:
idx = (states == dat.CPG_NAN) | (dists > wlen // 2)
states_wlen = np.ma.masked_array(states, idx)
group = out_group.create_group('win_stats/%d' % wlen)
for name, fun in six.iteritems(win_stats_meta):
stat = fun[0](states_wlen)
if hasattr(stat, 'mask'):
idx = stat.mask
stat = stat.data
if np.sum(idx):
stat[idx] = dat.CPG_NAN
group.create_dataset(name, data=stat, dtype=fun[1],
compression='gzip')
if annos:
log.info('Adding annotations ...')
group = in_group.create_group('annos')
for name, anno in six.iteritems(annos):
group.create_dataset(name, data=anno[chunk_idx],
dtype='int8',
compression='gzip')
"""
#chunk_file.close()
flat_dict={}
flatten_dict(yield_dict, flat_dict, no_prefix = True)
yield flat_dict
log.info('Done preprocessing!')
