def find_tad(self, experiments, weights=None, name=None, n_cpus=1,
verbose=True, max_tad_size="max", heuristic=True,
batch_mode=False, **kwargs):
"""
Call the :func:`pytadbit.tadbit.tadbit` function to calculate the
position of Topologically Associated Domain boundaries
:param experiment: A square matrix of interaction counts of Hi-C
data or a list of such matrices for replicated experiments. The
counts must be evenly sampled and not normalized. 'experiment'
can be either a list of lists, a path to a file or a file handler
:param True normalized: if False simple normalization will be computed,
as well as a simple column filtering will be applied (remove columns
where value at the diagonal is null)
:param 1 n_cpus: The number of CPUs to allocate to TADbit. If
n_cpus='max' the total number of CPUs will be used
:param max max_tad_size: an integer defining the maximum size of a
TAD. Default (auto) defines it as the number of rows/columns
:param True heuristic: whether to use or not some heuristics
:param False batch_mode: if True, all the experiments will be
concatenated into one for the search of TADs. The resulting TADs
found are stored under the name 'batch' plus a concatenation of the
experiment names passed (e.g.: if experiments=['exp1', 'exp2'], the
name would be: 'batch_exp1_exp2').
"""
experiments = experiments or self.experiments
if not isinstance(experiments, list):
experiments = [experiments]
xprs = []
for xpr in experiments:
if not isinstance(xpr, Experiment):
xpr = self.get_experiment(xpr)
xprs.append(xpr)
# if normalized and (not xpr._zeros or not xpr._normalization):
# raise Exception('ERROR: Experiments should be normalized, and' +
# ' filtered first')
if len(xprs) <= 1 and batch_mode:
raise Exception('ERROR: batch_mode implies that more than one ' +
'experiment is passed')
if batch_mode:
matrix = []
if not name:
name = 'batch'
resolution = xprs[0].resolution
for xpr in sorted(xprs, key=lambda x: x.name):
if xpr.resolution != resolution:
raise Exception('All Experiments must have the same ' +
'resolution\n')
matrix.append(xpr.hic_data[0])
if name.startswith('batch'):
name += '_' + xpr.name
siz = xprs[0].size
tmp = reduce(lambda x, y: x.__add__(y, silent=True), xprs)
tmp.filter_columns(silent=kwargs.get('silent', False))
remove = tuple([1 if i in tmp._zeros else 0
for i in xrange(siz)])
result = tadbit(matrix,
remove=remove,
n_cpus=n_cpus, verbose=verbose,
max_tad_size=max_tad_size,
no_heuristic=not heuristic, **kwargs)
xpr = Experiment(name, resolution, hic_data=matrix,
tad_def=result, **kwargs)
xpr._zeros = xprs[0]._zeros
for other in xprs[1:]:
xpr._zeros = dict([(k, None) for k in
set(xpr._zeros.keys()).intersection(
other._zeros.keys())])
self.add_experiment(xpr)
return
for xpr in xprs:
result = tadbit(
xpr.hic_data,
remove=tuple([1 if i in xpr._zeros else 0 for i in
xrange(xpr.size)]),
n_cpus=n_cpus, verbose=verbose,
max_tad_size=max_tad_size,
no_heuristic=not heuristic, **kwargs)
xpr.load_tad_def(result)
self._get_forbidden_region(xpr)
def find_tad(self, experiments, name=None, n_cpus=1,
verbose=True, max_tad_size="max", heuristic=True,
batch_mode=False, **kwargs):
"""
Call the :func:`pytadbit.tadbit.tadbit` function to calculate the
position of Topologically Associated Domain boundaries
:param experiment: A square matrix of interaction counts of Hi-C
data or a list of such matrices for replicated experiments. The
counts must be evenly sampled and not normalized. 'experiment'
can be either a list of lists, a path to a file or a file handler
:param True normalized: if False simple normalization will be computed,
as well as a simple column filtering will be applied (remove columns
where value at the diagonal is null)
:param 1 n_cpus: The number of CPUs to allocate to TADbit. If
n_cpus='max' the total number of CPUs will be used
:param max max_tad_size: an integer defining the maximum size of a
TAD. Default (auto) defines it as the number of rows/columns
:param True heuristic: whether to use or not some heuristics
:param False batch_mode: if True, all the experiments will be
concatenated into one for the search of TADs. The resulting TADs
found are stored under the name 'batch' plus a concatenation of the
experiment names passed (e.g.: if experiments=['exp1', 'exp2'], the
name would be: 'batch_exp1_exp2').
"""
experiments = experiments or self.experiments
if not isinstance(experiments, list):
experiments = [experiments]
xprs = []
for xpr in experiments:
if not isinstance(xpr, Experiment):
xpr = self.get_experiment(xpr)
xprs.append(xpr)
# if normalized and (not xpr._zeros or not xpr._normalization):
# raise Exception('ERROR: Experiments should be normalized, and' +
# ' filtered first')
if len(xprs) <= 1 and batch_mode:
raise Exception('ERROR: batch_mode implies that more than one ' +
'experiment is passed')
if batch_mode:
matrix = []
if not name:
name = 'batch'
resolution = xprs[0].resolution
for xpr in sorted(xprs, key=lambda x: x.name):
if xpr.resolution != resolution:
raise Exception('All Experiments must have the same ' +
'resolution\n')
matrix.append(xpr.hic_data[0])
if name.startswith('batch'):
name += '_' + xpr.name
siz = xprs[0].size
tmp = reduce(lambda x, y: x.__add__(y, silent=True), xprs)
tmp.filter_columns(silent=kwargs.get('silent', False))
remove = tuple([1 if i in tmp._zeros else 0
for i in range(siz)])
result = tadbit(matrix,
remove=remove,
n_cpus=n_cpus, verbose=verbose,
max_tad_size=max_tad_size,
no_heuristic=not heuristic, **kwargs)
xpr = Experiment(name, resolution, hic_data=matrix,
tad_def=result, **kwargs)
xpr._zeros = xprs[0]._zeros
for other in xprs[1:]:
xpr._zeros = dict([(k, None) for k in
set(xpr._zeros.keys()).intersection(
list(other._zeros.keys()))])
self.add_experiment(xpr)
return
for xpr in xprs:
result = tadbit(
xpr.hic_data,
remove=tuple([1 if i in xpr._zeros else 0 for i in
range(xpr.size)]),
n_cpus=n_cpus, verbose=verbose,
max_tad_size=max_tad_size,
no_heuristic=not heuristic, **kwargs)
xpr.load_tad_def(result)
self._get_forbidden_region(xpr)
