def epose_prediction(Arguments):
'''
Score some new mutations with the ePOSE(s) you already created. First we'll take our ePOSEs and the mutations originally used
create them to derive the linear equation necessary to convert POSE scores to predicted endophenotypes. Finally, we score the new
mutations and use said linear equation to supply predictions.
'''
#First get the cutoff from the mutations originally used to make the ePOSE
Mutations, Sequences, ReferenceGene, Identities, ResidueBurial, Annotation = make_pose_input(Arguments)
#These mutations are the ones we actually need.
Mutations, ePOSEs = zip(*[(cPickle.load(open(File, "rb"))[0], cPickle.load(open(File, "rb"))[1][0]) \
for File in ls("./") if Arguments.Filename in File])
Mutations, Endophenotypes = Mutations[0].keys(), Mutations[0].values()
LinearParameters = []
ePOSEScores = []
for ePOSE in ePOSEs:
MSA = zip(*[Sequences[Gene] for Gene in ePOSE]) #Turn the list of genes into the MSA (sequence ensemble)
Scores = get_pose_scores(MSA, list(ePOSE), Identities, Mutations, ResidueBurial, Annotation, Arguments)
ePOSEScores.append(Scores)
a, b, RR = linear_regression(Scores, Endophenotypes)
LinearParameters.append((a, b))
ePOSEScores = [mean(Score) for Score in zip(*ePOSEScores)]
#Now we are ready to get the new mutations.
Mutations, Sequences, ReferenceGene, Identities, ResidueBurial, Annotation = make_pose_input(Arguments)
Scores = []
for ePOSE in ePOSEs:
MSA = zip(*[Sequences[Gene] for Gene in ePOSE]) #Turn the list of genes into the MSA (sequence ensemble)
Scores.append(get_pose_scores(MSA, list(ePOSE), Identities, Mutations, ResidueBurial, Annotation, Arguments))
Scores = dict(zip(Mutations, zip(*Scores)))
print "Mutation, Mean POSE score, Standard deviation, Predicted Endophenotype, Standard deviation"
for Mutation in Mutations:
PredictedEndophenotypes = [a*mean(Scores[Mutation]) + b for a, b in LinearParameters]
print Mutation, "\t", "\t", round(mean(Scores[Mutation]), 3), "\t", "\t", round(std(Scores[Mutation]), 3), \
"\t", "\t", "\t", round(mean(PredictedEndophenotypes), 3), "\t", "\t", "\t", round(std(PredictedEndophenotypes), 3)
print " "
print "~~~~~~~~~~ Performance Metrics ~~~~~~~~~~~~~"
print " "
print "R-squared =", linear_regression(ePOSEScores, Endophenotypes)[2]
print "Pearson correlation =", correlation(ePOSEScores, Endophenotypes)
print "P-value =", correlation_pvalue(ePOSEScores, Endophenotypes)
return
def pose_prediction(Arguments):
'''
Score some new mutations with the POSE(s) you already created. First we need to determine the optimal POSE
score cutoff for predicting a mutants phenotype; this is done using the mutations used to create the POSE in
the first place. Next, we score the mutations and apply said cutoffs to make predictions.
'''
#First get the cutoff from the mutations originally used to make the POSE
Mutations, Sequences, ReferenceGene, Identities, ResidueBurial, Annotation = make_pose_input(Arguments)
#These mutations are the ones we actually need.
Mutations, POSEs = zip(*[(cPickle.load(open(File, "rb"))[0], cPickle.load(open(File, "rb"))[1][0]) \
for File in ls("./") if Arguments.Filename in File])
Mutations, Phenotypes = Mutations[0].keys(), Mutations[0].values()
Scores = []
for POSE in POSEs:
MSA = zip(*[Sequences[Gene] for Gene in POSE]) #Turn the list of genes into the MSA (sequence ensemble)
Scores.append(get_pose_scores(MSA, list(POSE), Identities, Mutations, ResidueBurial, Annotation, Arguments))
Scores = [mean(Score) for Score in zip(*Scores)]
AUC, Metrix = ROC(Scores, Phenotypes, Arguments.Correlated)
Optimal = sorted(Metrix, key=lambda Metric: Metric.Sensitivity + Metric.Specificity, reverse=True)[0]
#Now we are ready to get the new mutations.
Mutations, Sequences, ReferenceGene, Identities, ResidueBurial, Annotation = make_pose_input(Arguments)
Scores = []
for POSE in POSEs:
MSA = zip(*[Sequences[Gene] for Gene in POSE]) #Turn the list of genes into the MSA (sequence ensemble)
Scores.append(get_pose_scores(MSA, list(POSE), Identities, Mutations, ResidueBurial, Annotation, Arguments))
Scores = dict(zip(Mutations, zip(*Scores)))
print "Mutation, Mean POSE score, Standard deviation, Predicted phenotype"
for Mutation in Mutations:
if Arguments.Correlated and mean(Scores[Mutation]) >= Optimal.CutOff:
print Mutation, "\t", "\t", round(mean(Scores[Mutation]), 3), "\t", "\t", round(std(Scores[Mutation]), 3), "\t", "\t", "Positive"
if Arguments.Correlated and mean(Scores[Mutation]) < Optimal.CutOff:
print Mutation, "\t", "\t", round(mean(Scores[Mutation]), 3), "\t", "\t", round(std(Scores[Mutation]), 3), "\t", "\t", "Negative"
if not Arguments.Correlated and mean(Scores[Mutation]) <= Optimal.CutOff:
print Mutation, "\t", "\t", round(mean(Scores[Mutation]), 3), "\t", "\t", round(std(Scores[Mutation]), 3), "\t", "\t", "Positive"
if not Arguments.Correlated and mean(Scores[Mutation]) > Optimal.CutOff:
print Mutation, "\t", "\t", round(mean(Scores[Mutation]), 3), "\t", "\t", round(std(Scores[Mutation]), 3), "\t", "\t", "Negative"
print " "
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print "Cutoff used for separating the classes (determined when POSEs were initially created!!!) =", round(Optimal.CutOff, 2)
print " "
print "Performance acheived on the mutations used to train the initial POSE:"
print " "
print "\t", "Area under the ROC curve =", round(AUC, 2)
print "\t", "Accuracy =", Optimal.Accuracy
print "\t", "Sensitivity =", Optimal.Sensitivity
print "\t", "Specificity =", Optimal.Specificity
print "\t", "Negative predictive value =", Optimal.NPV
print "\t", "Positive predictive value =", Optimal.PPV
print "\t", "Mathews correlation coefficient =", Optimal.MCC
return