def validate_markers(Arguments):
'''
'''
Labels, Features, Variates, Phenotypes, Markers = get_supervised_dataset(Arguments)
Header, Results = load_results_file(get_path("ValidateMarkers"))
CSVfile = open(Arguments.Filename + ".csv", "wb")
CSVwriter = csv.writer(CSVfile, dialect='excel')
CSVwriter.writerow(["Union","Intersection","Difference","Interaction", "Phenotype", "P-value", "Odds Ratio", "Effect Size",
"Sensitivity","Specificity","PPV","NPV","Accuracy", "MCC", "Sample Count", "Case Count"])
for Phenotype in Phenotypes:
Response = Variates[Features.index(Phenotype)]
for Marker in Results:
try:
Predictor = assemble_setwork(Features, Variates,
filter(None, Marker[Header.index("Union")].split(", ")),
filter(None, Marker[Header.index("Intersection")].split(", ")),
filter(None, Marker[Header.index("Difference")].split(", ")), Arguments)
TP,FP,FN,TN = contingency_table(Predictor, Response, NA=Arguments.NA)
performance = Performance(Marker[Header.index("Interaction")], TP,FP,FN,TN)
effect_size = EffectSize(Marker[Header.index("Interaction")], TP,FP,FN,TN)
CSVwriter.writerow([Marker[Header.index("Union")], Marker[Header.index("Intersection")], Marker[Header.index("Difference")],
Marker[Header.index("Interaction")], Phenotype[:Phenotype.index(":")], "%0.2e" %fisher(TP,FP,FN,TN).two_tail,
"%0.2f" %effect_size.odds_ratio, "%0.2f" %effect_size.difference_of_proportions, "%0.2f" %performance.sensitivity,
"%0.2f" %performance.specificity, "%0.2f" %performance.PPV, "%0.2f" %performance.NPV,
"%0.2f" %performance.accuracy, "%0.2f" %performance.MCC, TP+FP+FN+TN, TP+FN])
except ValueError:
CSVwriter.writerow([Marker[Header.index("Union")], Marker[Header.index("Intersection")], Marker[Header.index("Difference")], "NA"])
CSVfile.close()
return
def leave_some_out(Arguments):
'''
Function for selecting biomarkers from cross-validation output. Strict in that it only returns
biomarkers that were selected during each cross validation. Might have the benefit, relative
to vote-based prediction, that these were so predictive that they will translate better to future
predictions. Also lends itself to simple clinical use because they require little or no computational
support for subsequent prediction...it is simply the marker
'''
CrossValidations = load_validation_data(Arguments)
Labels, Features, Variates, Phenotypes, Markers = get_supervised_dataset(Arguments)
Phenotype = Phenotypes[0]
Response = Variates[Features.index(Phenotype)]
Setworks = {}
for CrossValidation in CrossValidations:
for Barcode in CrossValidations[CrossValidation]["Barcodes"]:
Setwork = (CrossValidations[CrossValidation]["UnionFeatures"][Barcode], \
CrossValidations[CrossValidation]["IntersectionFeatures"][Barcode], \
CrossValidations[CrossValidation]["DifferenceFeatures"][Barcode], \
CrossValidations[CrossValidation]["Interactions"][Barcode])
if Setworks.has_key(Setwork): Setworks[Setwork].append(Barcode)
else: Setworks[Setwork] = [Barcode]
PValues = {}
QValues = {}
Performances = {}
Interactions = {}
FeatureVectors = {}
UnionFeatures = {}
IntersectionFeatures = {}
DifferenceFeatures = {}
SampleCounts = {}
CaseCounts = {}
EffectSizes = {}
Barcodes = []
for Setwork in Setworks:
if len(Setworks[Setwork]) == len(CrossValidations): #Sework had to be selected in each cross validation!!!
Union, Intersection, Difference, Interaction = Setwork
Predictor = assemble_setwork(Features, Variates, Union, Intersection, Difference, Arguments)
TP,FP,FN,TN = contingency_table(Predictor, Response, NA=Arguments.NA)
Barcode = Setworks[Setwork][0]
Barcodes.append(Barcode)
PValues[Barcode] = fisher(TP,FP,FN,TN).two_tail
QValues[Barcode] = "NA"
Performances[Barcode] = Performance(Interaction, TP,FP,FN,TN)
Interactions[Barcode] = Interaction
EffectSizes[Barcode] = EffectSize(Interactions[Barcode], TP,FP,FN,TN)
FeatureVectors[Barcode] = Predictor
UnionFeatures[Barcode] = Union
IntersectionFeatures[Barcode] = Intersection
DifferenceFeatures[Barcode] = Difference
SampleCounts[Barcode] = TP + FP + FN + TN
CaseCounts[Barcode] = TP + FN
Results = {}
Results["PValues"] = PValues
Results["QValues"] = QValues
Results["Performances"] = Performances
Results["Interactions"] = Interactions
Results["FeatureVectors"] = FeatureVectors
Results["UnionFeatures"] = UnionFeatures
Results["IntersectionFeatures"] = IntersectionFeatures
Results["DifferenceFeatures"] = DifferenceFeatures
Results["SampleCounts"] = SampleCounts
Results["CaseCounts"] = CaseCounts
Results["EffectSizes"] = EffectSizes
#Doesn't matter which index we use we just need one report. The last accessed 'CrossValidation' will do.
Results["Report"] = make_report(Labels, CrossValidations[CrossValidation]["Report"])
Results["Labels"] = Labels
Results["Barcodes"] = Barcodes
Results["Phenotype"] = Response
if Arguments.Filename.lower() == "default":
DataTypes = set(Arguments.Data).difference(set([Arguments.Phenotype]))
Pickle = "_".join(["Phenotype=" + Phenotype[:Phenotype.index(":")],
"_".join(sorted(DataTypes)), str(Arguments.FeatureMin), Arguments.CorrectionMethod,
"".join(map(str, Arguments.BooleanSets)), "".join(map(str, Arguments.Optimization))])
else:
Pickle = Arguments.Filename + "_" + Phenotype[:Phenotype.index(":")]
cPickle.dump(Results, open(get_path("MOCA.results") + "/" + Pickle, "wb"), -1)
'''
#Get rid of barcodes that for setworks that didn't pass a performance threshold (if provided)
Barcodes = [Barcode for Barcode in Barcodes if minimum_performance(Performances[Barcode], Arguments)]
#Initial sort will be done by decreasing balanced accuracy
Barcodes = sorted(Barcodes, key=lambda Barcode: \
(Performances[Barcode].sensitivity + Performances[Barcode].specificity)/2, reverse=True)
CSVfile = open(Arguments.Filename + ".csv", "wb")
CSVwriter = csv.writer(CSVfile, dialect='excel')
#Right the excel header
CSVwriter.writerow(["Union","Intersection","Difference","Interaction", "Phenotype",
"Sensitivity","Specificity","PPV","NPV","Accuracy", "Sample Count"])
for Barcode in Barcodes:
p = Performances[Barcode]
Sens, Spec, PPV, NPV, Accuracy = p.sensitivity, p.specificity, p.PPV, p.NPV, p.accuracy
CSVwriter.writerow([", ".join(UnionFeatures[Barcode]), ", ".join(IntersectionFeatures[Barcode]),
", ".join(DifferenceFeatures[Barcode]), Interactions[Barcode],
Phenotype[:Phenotype.index(":")], "%0.2f" %Sens, "%0.2f" %Spec,
"%0.2f" %PPV, "%0.2f" %NPV, "%0.2f" %Accuracy, SampleCount[Barcode]])
CSVfile.close()
'''
return
