def make_pose_input(Arguments):
'''
Gather all the data required as input for (e)POSE derivation.
'''
Mutations = get_mutations(
Arguments
) #Dictionary of amino acid substitutions and binary pheontypes or endophenotypes
Sequences = get_sequences(
Arguments) #Load the fasta formatted sequence file
ReferenceGene = get_reference_gene(Arguments) #Gene your scoring
Identities = get_identities(
ReferenceGene, Sequences,
Arguments) #%ID of all sequences relative to ref
#Inititialize burial, and populate if called
ResidueBurial = {}
if Arguments.Structure:
ResidueBurial = normalized_residue_burial(Arguments)
#Inititialize annotate, and populate if called
Annotation = {}
if Arguments.Annotation: Annotation = get_annotation(Arguments)
return Mutations, Sequences, ReferenceGene, Identities, ResidueBurial, Annotation
def leave_some_out(Arguments):
'''
Divide up the mutations before sending off to cross validation.
'''
Mutations = get_mutations(Arguments)
CrossValidations = int(
ceil(len(Mutations.keys()) / float(Arguments.LeaveSomeOut)))
#For a standard POSE, the trick is keeping an as-balanced-as-possible spliting of the classes for each data split
if Arguments.Mode == "POSE":
Positive = [
Mutation for Mutation, Phenotype in Mutations.items() if Phenotype
]
Negative = [
Mutation for Mutation, Phenotype in Mutations.items()
if not Phenotype
]
shuffle(
Positive
) #Get rid of bias that MIGHT be inherent in the original order of the mutations
#Split as evenly as possible among the cross-validations
Positive = dict([(CrossValidation, Positive[Mutation:len(Positive):CrossValidations]) \
for CrossValidation, Mutation in enumerate(range(CrossValidations))])
shuffle(
Negative
) #Get rid of bias that MIGHT be inherent in the original order of the mutations
#Split as evenly as possible among the cross-validations
Negative = dict([(CrossValidation, Negative[Mutation:len(Negative):CrossValidations]) \
for CrossValidation, Mutation in enumerate(range(CrossValidations))])
#only for the leave ONE out case do we do cases and controls in series
if Arguments.LeaveSomeOut == 1:
if len(Positive) < len(Negative):
Postive = dict(
zip(Positive.keys(), list(reversed(Positive.values()))))
else:
Negative = dict(
zip(Negative.keys(), list(reversed(Negative.values()))))
HoldoutMutations = dict([(CrossValidation, Positive[CrossValidation] + Negative[CrossValidation]) \
for CrossValidation in Positive.keys()])
#For ePOSEs it is simpler. Everyone is endophenotype positive. We therefore only have one class.
if Arguments.Mode == "ePOSE":
Positive = Mutations.keys()
shuffle(
Positive
) #Get rid of bias that MIGHT be inherent in the original order of the mutations
HoldoutMutations = dict([(CrossValidation, Positive[Mutation:len(Positive):CrossValidations]) \
for CrossValidation, Mutation in enumerate(range(CrossValidations))])
cross_validation(HoldoutMutations, Arguments)
return
def leave_some_out(Arguments):
'''
Divide up the mutations before sending off to cross validation.
'''
Mutations = get_mutations(Arguments)
CrossValidations = int(ceil(len(Mutations.keys())/float(Arguments.LeaveSomeOut)))
#For a standard POSE, the trick is keeping an as-balanced-as-possible spliting of the classes for each data split
if Arguments.Mode == "POSE":
Positive = [Mutation for Mutation, Phenotype in Mutations.items() if Phenotype]
Negative = [Mutation for Mutation, Phenotype in Mutations.items() if not Phenotype]
shuffle(Positive) #Get rid of bias that MIGHT be inherent in the original order of the mutations
#Split as evenly as possible among the cross-validations
Positive = dict([(CrossValidation, Positive[Mutation:len(Positive):CrossValidations]) \
for CrossValidation, Mutation in enumerate(range(CrossValidations))])
shuffle(Negative) #Get rid of bias that MIGHT be inherent in the original order of the mutations
#Split as evenly as possible among the cross-validations
Negative = dict([(CrossValidation, Negative[Mutation:len(Negative):CrossValidations]) \
for CrossValidation, Mutation in enumerate(range(CrossValidations))])
#only for the leave ONE out case do we do cases and controls in series
if Arguments.LeaveSomeOut == 1:
if len(Positive) < len(Negative):
Postive = dict(zip(Positive.keys(), list(reversed(Positive.values()))))
else:
Negative = dict(zip(Negative.keys(), list(reversed(Negative.values()))))
HoldoutMutations = dict([(CrossValidation, Positive[CrossValidation] + Negative[CrossValidation]) \
for CrossValidation in Positive.keys()])
#For ePOSEs it is simpler. Everyone is endophenotype positive. We therefore only have one class.
if Arguments.Mode == "ePOSE":
Positive = Mutations.keys()
shuffle(Positive) #Get rid of bias that MIGHT be inherent in the original order of the mutations
HoldoutMutations = dict([(CrossValidation, Positive[Mutation:len(Positive):CrossValidations]) \
for CrossValidation, Mutation in enumerate(range(CrossValidations))])
cross_validation(HoldoutMutations, Arguments)
return
def make_pose_input(Arguments):
'''
Gather all the data required as input for (e)POSE derivation.
'''
Mutations = get_mutations(Arguments) #Dictionary of amino acid substitutions and binary pheontypes or endophenotypes
Sequences = get_sequences(Arguments) #Load the fasta formatted sequence file
ReferenceGene = get_reference_gene(Arguments) #Gene your scoring
Identities = get_identities(ReferenceGene, Sequences, Arguments) #%ID of all sequences relative to ref
#Inititialize burial, and populate if called
ResidueBurial = {}
if Arguments.Structure: ResidueBurial = normalized_residue_burial(Arguments)
#Inititialize annotate, and populate if called
Annotation = {}
if Arguments.Annotation: Annotation = get_annotation(Arguments)
return Mutations, Sequences, ReferenceGene, Identities, ResidueBurial, Annotation
