def seq_err(self, member):
"""
Calculate the probability of sequencing error. Assume each chromosome
is equally-likely to be sequenced.
The probability is drawn from a Dirichlet multinomial distribution:
This is a point of divergence from the Cartwright et al. paper
mentioned in the other functions.
When the Dirichlet multinomial is called, the max element is stored in
max_elems, so that the scaling of the probability matrix can be
manipulated later.
Args:
member: Integer representing index of the read counts for a
family member in the trio model.
Returns:
1 x 16 probability vector that needs to be multiplied by a
transition matrix.
"""
# TODO: add bias when alpha freq are added
alpha_mat = ut.get_alphas(self.seq_err_rate) * self.dm_disp
prob_mat = np.zeros((ut.GENOTYPE_COUNT))
for i, alpha in enumerate(alpha_mat):
log_proba = ut.dirichlet_multinomial(alpha, self.reads[member])
prob_mat[i] = log_proba
prob_mat_rescaled, max_elem = ut.normalspace(prob_mat)
self.max_elems.append(max_elem)
return prob_mat_rescaled
def seq_err(self, member):
"""
Calculate the probability of sequencing error. Assume each chromosome
is equally-likely to be sequenced.
The probability is drawn from a Dirichlet multinomial distribution:
This is a point of divergence from the Cartwright et al. paper
mentioned in the other functions.
When the Dirichlet multinomial is called, the max element is stored in
max_elems, so that the scaling of the probability matrix can be
manipulated later.
Args:
member: Integer representing index of the read counts for a
family member in the trio model.
Returns:
1 x 16 probability vector that needs to be multiplied by a
transition matrix.
"""
# TODO: add bias when alpha freq are added
alpha_mat = ut.get_alphas(self.seq_err_rate) * self.dm_disp
prob_mat = np.zeros((ut.GENOTYPE_COUNT))
for i, alpha in enumerate(alpha_mat):
log_proba = ut.dirichlet_multinomial(alpha, self.reads[member])
prob_mat[i] = log_proba
prob_mat_rescaled, max_elem = ut.normalspace(prob_mat)
self.max_elems.append(max_elem)
return prob_mat_rescaled
def dm_sample(self, soma_idx):
"""
Use alpha frequencies based on the somatic genotype to select
nucleotide frequencies and use these frequencies to draw sequencing
reads at a specified coverage (Dirichlet multinomial).
Args:
soma_idx: Index of somatic genotype to get the appropriate alpha
frequencies.
Returns:
Array containing read counts [#A, #C, #G, #T].
"""
alpha_mat = (ut.get_alphas(self.trio_model.seq_err_rate) *
self.trio_model.dm_disp)
alpha = np.random.dirichlet(alpha_mat[soma_idx])
return np.random.multinomial(self.cov, alpha)
def dm_sample(self, soma_idx):
"""
Use alpha frequencies based on the somatic genotype to select
nucleotide frequencies and use these frequencies to draw sequencing
reads at a specified coverage (Dirichlet multinomial).
Args:
soma_idx: Index of somatic genotype to get the appropriate alpha
frequencies.
Returns:
Array containing read counts [#A, #C, #G, #T].
"""
alpha_mat = (ut.get_alphas(self.trio_model.seq_err_rate) *
self.trio_model.dm_disp)
alpha = np.random.dirichlet(alpha_mat[soma_idx])
return np.random.multinomial(self.cov, alpha)
