def __get_counts(database, D1, D2, verbose):
"""Extract counts of patients as a distribution of the final age."""
# Patients with D1, D2 or both.
D1orD2_patients = database.query_emr_data([D1, D2], OR_match=True)
# Patients without D1 and without D2.
noD1D2_patients = database.query_emr_data(["not " + D1, "not " + D2])
# Extract counts of patients as a distribution of the final
# age value, seperately for each of 4 disease phenotypes.
joint_final_age = JointFinalAge(D1orD2_patients, noD1D2_patients, verbose)
final_age_array = joint_final_age.get_final_age_array()
patient_counts = joint_final_age.get_patient_counts()
return (patient_counts, final_age_array)
def __get_counts(database, D1, D2, verbose):
"""Extract counts of patients as a distribution of the final age."""
# Patients with D1, D2 or both.
D1orD2_patients = database.query_emr_data([D1, D2], OR_match = True)
# Patients without D1 and without D2.
noD1D2_patients = database.query_emr_data(["not " + D1, "not " + D2])
# Extract counts of patients as a distribution of the final
# age value, seperately for each of 4 disease phenotypes.
joint_final_age = JointFinalAge(D1orD2_patients, noD1D2_patients, verbose)
final_age_array = joint_final_age.get_final_age_array()
patient_counts = joint_final_age.get_patient_counts()
return (patient_counts, final_age_array)
def setup_log_likelihood_func(self,
database,
D1,
D2,
tau1,
tau2,
overlap_type,
threshold_type,
prevalence_file=None,
norm_prval_method=None):
"""Setup the log_likelihood function.
Parameters
----------
database : EMRDatabase class object
This class allow query of EMR data by diseases, gender, and
ethnicity citeria
D1 : string
Name of the first disease.
D2 : string
Name of the first disease.
tau1 : integer
Model's parameter indicating the minimum number of deleterious
mutation in S1 or S12.
tua2 : integer
Model's parameter indicating the minimum number of deleterious
mutation in S2 or S12.
overlap_type : ["independent", "cooperation", "competition"]
Model's parameter describing the type of genetic overlap used in
the model
threshold_type : ["sharp", "soft"]
Model's parameter describing the type of genetic penetrance used in
the model
prevalence_file : string
Location of the prevalence data file
norm_prval_method : [None, "rzhetsky", "max", "min",
"avg", "wts_avg", "sum"]
Protocol to normalize the prevalence data.
"""
# Query patients data
D1orD2_patients = database.query_emr_data([D1, D2], OR_match=True)
noD1D2_patients = database.query_emr_data(["not " + D1, "not " + D2])
# Conditional probabilities: P(phi(t) | phi(infty))
joint_age_of_onset = JointAgeOfOnset(D1orD2_patients)
joint_age_of_onset_funcs = joint_age_of_onset.get_funcs()
# Patient counts of as a distribution of the final age value.
joint_final_age = JointFinalAge(D1orD2_patients, noD1D2_patients)
final_age_array = joint_final_age.get_final_age_array()
patient_counts = joint_final_age.get_patient_counts()
# Setup the genetic_penetrance model to compute the age-integrated
# phenotype probabilities: P(phi(infty) ; rho1, rho2, rho12)
genetic_penetrance = GeneticPenetrance(tau1, tau2, overlap_type,
threshold_type)
# Create a function that will normalize the raw EMR data to match the
# general population disease prevalence.
if prevalence_file != None and norm_prval_method != None:
prval_norm_func = (create_prval_norm_func(
D1, D2, database.get_disease2count(),
database.get_tot_patient_count(), prevalence_file,
norm_prval_method))
else:
prval_norm_func = None
# Function to evaluate log-likelihood at given rho1, rho2 and rho12.
log_likelihood_func = (create_log_likelihood_func(
genetic_penetrance, joint_age_of_onset_funcs, patient_counts,
final_age_array, prval_norm_func))
# Function to evaluate the derivative of the log-likelihod wrt
# to rho1, rho2 and rho12.
log_likelihood_fprime = (create_log_likelihood_fprime(
genetic_penetrance, joint_age_of_onset_funcs, patient_counts,
final_age_array, prval_norm_func))
self.tau1 = tau1
self.tau2 = tau2
self.overlap_type = overlap_type
self.log_likelihood_func = log_likelihood_func
self.log_likelihood_fprime = log_likelihood_fprime
def setup_log_likelihood_func(self,
database,
D1, D2,
tau1, tau2,
overlap_type,
threshold_type,
prevalence_file=None,
norm_prval_method=None):
"""Setup the log_likelihood function.
Parameters
----------
database : EMRDatabase class object
This class allow query of EMR data by diseases, gender, and
ethnicity citeria
D1 : string
Name of the first disease.
D2 : string
Name of the first disease.
tau1 : integer
Model's parameter indicating the minimum number of deleterious
mutation in S1 or S12.
tua2 : integer
Model's parameter indicating the minimum number of deleterious
mutation in S2 or S12.
overlap_type : ["independent", "cooperation", "competition"]
Model's parameter describing the type of genetic overlap used in
the model
threshold_type : ["sharp", "soft"]
Model's parameter describing the type of genetic penetrance used in
the model
prevalence_file : string
Location of the prevalence data file
norm_prval_method : [None, "rzhetsky", "max", "min",
"avg", "wts_avg", "sum"]
Protocol to normalize the prevalence data.
"""
# Query patients data
D1orD2_patients = database.query_emr_data([D1, D2], OR_match = True)
noD1D2_patients = database.query_emr_data(["not " + D1, "not " + D2])
# Conditional probabilities: P(phi(t) | phi(infty))
joint_age_of_onset = JointAgeOfOnset(D1orD2_patients)
joint_age_of_onset_funcs = joint_age_of_onset.get_funcs()
# Patient counts of as a distribution of the final age value.
joint_final_age = JointFinalAge(D1orD2_patients, noD1D2_patients)
final_age_array = joint_final_age.get_final_age_array()
patient_counts = joint_final_age.get_patient_counts()
# Setup the genetic_penetrance model to compute the age-integrated
# phenotype probabilities: P(phi(infty) ; rho1, rho2, rho12)
genetic_penetrance = GeneticPenetrance(tau1,
tau2,
overlap_type,
threshold_type)
# Create a function that will normalize the raw EMR data to match the
# general population disease prevalence.
if prevalence_file != None and norm_prval_method != None:
prval_norm_func = (
create_prval_norm_func(D1, D2,
database.get_disease2count(),
database.get_tot_patient_count(),
prevalence_file,
norm_prval_method))
else:
prval_norm_func = None
# Function to evaluate log-likelihood at given rho1, rho2 and rho12.
log_likelihood_func = (
create_log_likelihood_func(genetic_penetrance,
joint_age_of_onset_funcs,
patient_counts,
final_age_array,
prval_norm_func))
# Function to evaluate the derivative of the log-likelihod wrt
# to rho1, rho2 and rho12.
log_likelihood_fprime = (
create_log_likelihood_fprime(genetic_penetrance,
joint_age_of_onset_funcs,
patient_counts,
final_age_array,
prval_norm_func))
self.tau1 = tau1
self.tau2 = tau2
self.overlap_type = overlap_type
self.log_likelihood_func = log_likelihood_func
self.log_likelihood_fprime = log_likelihood_fprime