def computeATreeError(aMatrix, lafMatrix, afMatrix, realTree):
sampleNum = aMatrix.shape[1]
aObjMatrix = np.empty(aMatrix.shape, dtype=object)
#Convert the a matrix to an actual allele matrix
for row in range(0, aMatrix.shape[0]):
for col in range(0, aMatrix.shape[1]):
allele = aMatrix[row][col]
AOccurrences = [m.start() for m in re.finditer('A', allele)]
ACount = len(AOccurrences)
BOccurrences = [m.start() for m in re.finditer('B', allele)]
BCount = len(BOccurrences)
alleleObj = Alleles(ACount, BCount)
aObjMatrix[row][col] = alleleObj
#Compute the distance pairwise between samples
distanceMatrix = np.empty([sampleNum, sampleNum], dtype=float)
[chromosomes, positions, segmentation,
chromosomeArms] = parseReferenceFile()
for sample1 in range(0, sampleNum):
for sample2 in range(0, sampleNum):
#make a dummy sample object for the FST function
sample1Obj = Sample(None, None)
sample1Obj.measurements = LAF(lafMatrix[:, sample1], chromosomes,
positions, positions)
sample1Obj.measurements.segmentation = segmentation
sample1Obj.afMeasurements = afMatrix[:, sample1]
sample2Obj = Sample(None, None)
sample2Obj.measurements = LAF(lafMatrix[:, sample2], chromosomes,
positions, positions)
sample2Obj.measurements.segmentation = segmentation
sample2Obj.afMeasurements = afMatrix[:, sample2]
#The distance can be computed for the entire column at once using the FST
[messages,
dist] = FST().computeAlleleDistance(aObjMatrix[:, sample1],
aObjMatrix[:, sample2],
sample1Obj, sample2Obj)
distanceMatrix[sample1, sample2] = dist
#print distanceMatrix
#exit()
#Compute the MST
fullGraph = generateInitialTree(distanceMatrix, realTree.vertices)
mst = computeMST(fullGraph, realTree.vertices)
simulationErrorHandler = SimulationErrorHandler()
treeScore = simulationErrorHandler.computeTreeError([mst], realTree)
return treeScore
def computeATreeError(aMatrix, lafMatrix, afMatrix, realTree, chromosomes,
positions):
segmentationFile = simulationSettings.files['segmentationFile']
segmentation = Segmentation()
segmentation.setSegmentationFromFile(segmentationFile)
sampleNum = aMatrix.shape[1]
aObjMatrix = np.empty(aMatrix.shape, dtype=object)
#Convert the a matrix to an actual allele matrix
for row in range(0, aMatrix.shape[0]):
for col in range(0, aMatrix.shape[1]):
allele = aMatrix[row][col]
AOccurrences = [m.start() for m in re.finditer('A', allele)]
ACount = len(AOccurrences)
BOccurrences = [m.start() for m in re.finditer('B', allele)]
BCount = len(BOccurrences)
alleleObj = Alleles(ACount, BCount)
aObjMatrix[row][col] = alleleObj
#Compute the distance pairwise between samples
distanceMatrix = np.empty([sampleNum, sampleNum], dtype=float)
for sample1 in range(0, sampleNum):
for sample2 in range(0, sampleNum):
#make a dummy sample object for the FST function
sample1Obj = Sample(None, None)
sample1Obj.measurements = LAF(lafMatrix[:, sample1], chromosomes,
positions, positions)
sample1Obj.measurements.segmentation = segmentation
sample1Obj.afMeasurements = afMatrix[:, sample1]
sample2Obj = Sample(None, None)
sample2Obj.measurements = LAF(lafMatrix[:, sample2], chromosomes,
positions, positions)
sample2Obj.measurements.segmentation = segmentation
sample2Obj.afMeasurements = afMatrix[:, sample2]
#The distance can be computed for the entire column at once using the FST
[messages,
dist] = FST().computeAlleleDistance(aObjMatrix[:, sample1],
aObjMatrix[:, sample2],
sample1Obj, sample2Obj)
distanceMatrix[sample1, sample2] = dist
#print distanceMatrix
#exit()
#Compute the MST
fullGraph = generateInitialTree(distanceMatrix, realTree.vertices)
inferredTree = computeMST(fullGraph, realTree.vertices)
[
ancestrySwapErrorAbsentInInferred, ancestrySwapErrorPresentInInferred,
noOfSamplePairs
] = computeAncestrySwapError(realTree, inferredTree)
summedError = (ancestrySwapErrorAbsentInInferred +
ancestrySwapErrorPresentInInferred)
averagedAncestrySwapError = summedError / float(noOfSamplePairs)
#simulationErrorHandler = SimulationErrorHandler()
#treeScore = simulationErrorHandler.computeTreeError([mst], realTree)
return averagedAncestrySwapError
