def normalize_hic(self, iterations=0, max_dev=0.1, silent=False, factor=1):
"""
Normalize the Hi-C data.
It fills the Experiment.norm variable with the Hi-C values divided by
the calculated weight.
:param 0 iteration: number of iterations
:param 0.1 max_dev: iterative process stops when the maximum deviation
between the sum of row is equal to this number (0.1 means 10%)
:param False silent: does not warn when overwriting weights
:param 1 factor: final mean number of normalized interactions wanted
per cell (excludes filtered, or bad, out columns)
"""
bias = iterative(self, iterations=iterations,
max_dev=max_dev, bads=self.bads,
verbose=not silent)
if factor:
if not silent:
print 'rescaling to factor %d' % factor
# get the sum on half of the matrix
norm_sum = self.sum(bias)
# divide biases
target = (norm_sum / float(len(self) * len(self) * factor))**0.5
bias = dict([(b, bias[b] * target) for b in bias])
self.bias = bias
def normalize_hic(self, iterations=0, max_dev=0.1, silent=False, factor=1):
"""
Normalize the Hi-C data.
It fills the Experiment.norm variable with the Hi-C values divided by
the calculated weight.
:param 0 iteration: number of iterations
:param 0.1 max_dev: iterative process stops when the maximum deviation
between the sum of row is equal to this number (0.1 means 10%)
:param False silent: does not warn when overwriting weights
:param 1 factor: final mean number of normalized interactions wanted
per cell (excludes filtered, or bad, out columns)
"""
bias = iterative(self,
iterations=iterations,
max_dev=max_dev,
bads=self.bads,
verbose=not silent)
if factor:
if not silent:
print 'rescaling to factor %d' % factor
# get the sum on half of the matrix
norm_sum = self.sum(bias)
# divide biases
target = (norm_sum / float(len(self) * len(self) * factor))**0.5
bias = dict([(b, bias[b] * target) for b in bias])
self.bias = bias
def normalize_hic(self, iterations=0, max_dev=0.1, silent=False):
"""
Normalize the Hi-C data.
It fills the Experiment.norm variable with the Hi-C values divided by
the calculated weight.
:param 0 iteration: number of iterations
:param 0.1 max_dev: iterative process stops when the maximum deviation
between the sum of row is equal to this number (0.1 means 10%)
:param False silent: does not warn when overwriting weights
"""
self.bias = iterative(self, iterations=iterations,
max_dev=max_dev, bads=self.bads,
verbose=not silent)
def normalize_hic(self, iterations=0, max_dev=0.1, silent=False):
"""
Normalize the Hi-C data.
It fills the Experiment.norm variable with the Hi-C values divided by
the calculated weight.
:param 0 iteration: number of iterations
:param 0.1 max_dev: iterative process stops when the maximum deviation
between the sum of row is equal to this number (0.1 means 10%)
:param False silent: does not warn when overwriting weights
"""
self.bias = iterative(self, iterations=iterations,
max_dev=max_dev, bads=self.bads,
verbose=not silent)