def computeSNVTreeError(snvMatrix, cMatrix, lafMatrix, realTree):
sampleNum = snvMatrix.shape[1]
cObjMatrix = np.empty(cMatrix.shape, dtype=object)
for row in range(0, cMatrix.shape[0]):
for col in range(0, cMatrix.shape[1]):
currentC = cMatrix[row][col]
cObj = C([
2, int(currentC)
], []) #empty vector to stop initialization of allele combinations
dummyCMu = DummyCMu()
dummyCMu.c = cObj
cObjMatrix[row][col] = dummyCMu
[chromosomes, positions, variantIndices] = obtainSomaticVariantIndices()
#print variantIndices
#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 the sample objects. These now need somatic variants and a CMu
sample1Obj = Sample(None, None)
sample1Obj.bestCMu = cObjMatrix[:, sample1]
#the dummy c mu is actually a list of dummy c mu's, so we need to make one for each c
#dummyCMu = DummyCMu()
#dummyCMu.c = cObjMatrix[:,sample1]
#sample1Obj.bestCMu = dummyCMu
sample1Obj.somaticVariants = snvMatrix[:, sample1]
sample1Obj.somaticVariantsInd = variantIndices
sample1Obj.measurements = LAF(lafMatrix[:, sample1], chromosomes,
positions, positions)
sample2Obj = Sample(None, None)
#dummyCMu = DummyCMu()
#dummyCMu.c = cObjMatrix[:,sample2]
sample2Obj.bestCMu = cObjMatrix[:, sample2]
sample2Obj.somaticVariants = snvMatrix[:, sample2]
sample2Obj.somaticVariantsInd = variantIndices
sample2Obj.measurements = LAF(lafMatrix[:, sample2], chromosomes,
positions, positions)
#The distance can be computed for the entire column at once using the FST
[
messages, dist
] = SomaticVariantDistance().computeDistanceBetweenSomaticVariants(
sample1Obj, sample2Obj, sample1, sample2)
distanceMatrix[sample1, sample2] = dist
#Compute the MST
fullGraph = generateInitialTree(distanceMatrix, realTree.vertices)
mst = computeMST(fullGraph, realTree.vertices)
simulationErrorHandler = SimulationErrorHandler()
treeScore = simulationErrorHandler.computeTreeError([mst], realTree)
return treeScore
#obtain the chromosome, start and end information from the other samples
healthySample.measurements = LAF([0.5] * measurementLength,
tmpSamples[0].measurements.chromosomes,
tmpSamples[0].measurements.starts,
tmpSamples[0].measurements.ends)
healthySample.somaticVariants = [0] * somVarNum
healthySample.somaticVariantsInd = tmpSamples[0].somaticVariantsInd
healthySample.setParent(None)
healthySample.name = 'Precursor' #do not call it healthy, it may also be a 4N precursor.
#Make a dummy bestCMu for the healthy sample
eventDistances = targetClone.eventDistances
bestCMuHealthy = CMuCombination(C([2, precursorPloidy]),
Mu(precursorTumorFrequency), eventDistances)
healthySample.bestCMu = [bestCMuHealthy] * measurementLength
healthySample.originalCMu = healthySample.bestCMu
#Define all samples
samples = [healthySample] + tmpSamples
#get all somatic variants in one binary numpy array
allSomaticVariants = np.zeros((somVarNum, len(samples)))
for sample in range(0, len(samples)):
allSomaticVariants[:, sample] = samples[sample].somaticVariants
#also set the segmentation while we're at it
samples[sample].measurements.segmentation = segmentation
targetClone.segmentation = segmentation
#The run part will also draw the trees automatically.
