def calcTCI (mutcnaMatrixFN, degMatrixFN, tumorTypeFN = None, alphaNull = [1, 1], alphaIJKList = [2, 1, 1, 2], v0 = 0.3,
ppiDict = None, dictGeneLength = None, outputPath = ".", opFlag = None, PANCANFlag = None, rowBegin=0, rowEnd = None):
"""
calcTCI (mutcnaMatrix, degMatrix, alphaIJList, alphaIJKList, dictGeneLength)
Calculate the causal scores between each pair of SGA and DEG observed in each tumor
Inputs:
mutcnaMatrixFN A file containing a N x G binary matrix containing the mutation and CNA
data of all tumors. N is the number of tumors and
G is number of total number of unique genes. For a
tumor, genes that have SGAs are indicated by "1"s and "0"
otherwise.
Note the last 19 columns are indicators of the tumor
degMatrixFN A file contains a N x G' binary matrix representing DEG
status. A "1" indicate a gene is differentially expressed
in a tumor.
tumorTypeFN A string of filename. The file contains N x T matrix, in which
each row only has one element set to 1, rest to zero, as an indicator
which type of cancer each tumor belongs to
alphaIJList A list of Dirichlet hyperparameters defining the prior
that a mutation event occurs
alphaIJKList A list of Dirichlet hyperparameters for caulate the prior
of condition probability parameters. alphaIJK[0]: mut == 0 && deg == 0;
alphaIJK[1]: mut == 0 && deg == 1; alphaIJK[2]: mut == 1 && deg == 0;
alphaIJK[3]: mut == 1 && deg == 1
v0 A float scalar indicate the prior probability that a DEG
is caused by a non-SGA factor
PANCANFlag A boolean flag to indicate if we are doing PANCAN
ppiDict A dictionary keeps PPI network in the form an adjecency list (a dictionary of dictionary)
dictGeneLength A dictionary keeps the length of each of G genes in the
mutcnaMatrix
rowBegin, rowEnd These two arguments control allow user to choose which block out of all tumors (defined by the two
row numbers) will be processes in by this function. This can be used to process
mulitple block in a parallel fashion.
"""
# check if gene length dictionary is set
if not dictGeneLength :
print "Gene length dictionary not provided, quit\n"
sys.exit()
# read in data in the form of NamedMatrix
try:
mutcnaMatrix = NamedMatrix(mutcnaMatrixFN)
except:
print "Failed to import data matrix %s\n" % mutcnaMatrixFN
sys.exit()
try:
degMatrix = NamedMatrix(degMatrixFN)
except:
print "Failed to import data matrix %s\n" % degMatrixFN
sys.exit()
mutGeneNames = mutcnaMatrix.getColnames()
mutTumorNames = mutcnaMatrix.getRownames()
degGeneNames = degMatrix.getColnames()
exprsTumorNames = degMatrix.getRownames()
#check if same tumor names from two matrices above agree
if exprsTumorNames != mutTumorNames:
print "The tumors for mutcnaMatrix and degMatrix do not fully overlap!"
print degMatrix.getRownames()
print mutcnaMatrix.getRownames()
sys.exit()
tumorNames = exprsTumorNames
nTumors, nMutGenes = mutcnaMatrix.shape()
# now perform PANCAN analysis related tasks
if PANCANFlag:
if not tumorTypeFN:
print "Cannot perform PANCAN analysis without tumor-type-indicator matrix"
sys.exit()
try:
tumorTypeMatrix = NamedMatrix(tumorTypeFN)
except:
print "Failed to import tumor type file %s" % tumorTypeFN
sys.exit()
tumorTypeTumorNames = [x.replace("\"", "") for x in tumorTypeMatrix.getRownames()]
if exprsTumorNames != tumorTypeTumorNames:
print "The tumors for tumorTypeMatrix and degMatrix do not fully overlap!"
sys.exit()
tumorTypes = tumorTypeMatrix.getColnames()
# Calculate the prior probability that a tumor-type variable may influence a DEG
# to be proportional to the number of tumors from a given type
vt = np.sum(tumorTypeMatrix.data, 0) # perform a rowsum to count each type tumor
vtprior = np.divide(vt, float(nTumors)) # normalize to 1, as prior for each type of tumor
# Now start looping through a chunk of individual tumors and calculate the causal scores between each pair of SGA and DEG
print "Done with loading data, start processing tumor " + str(rowBegin)
if not rowEnd:
rowEnd = nTumors - 1
else:
if rowEnd >= nTumors:
rowEnd = nTumors - 1
elif rowEnd < rowBegin:
print "Invalid rowEnd < rowBegin arguments given."
sys.exit()
if rowBegin > rowEnd:
print "Invlid rowBegin > rowEnd argument given."
sys.exit()
for t in range(rowBegin, rowEnd):
print "processign tumor " + tumorNames[t]
#print pacifier
if t % 50 == 0:
print "\nProcessed %s tumors" % str(t)
# collect data related to DEGs to construct a submatrix containing only DEG of the tumor
degGeneIndx = [i for i, j in enumerate(degMatrix.data[t,:]) if j == 1]
tumorDEGGenes = [degGeneNames[i] for i in degGeneIndx]
tumorDEGMatrix = degMatrix.data[:,degGeneIndx]
# extract the sub-matrix of mutcnaMatrix that only contain the genes that are mutated in a given tumor t
tumormutGeneIndx = [i for i, j in enumerate(mutcnaMatrix.data[t,:]) if j == 1]
tumorMutGenes= [mutGeneNames[i] for i in tumormutGeneIndx]
nTumorMutGenes = len(tumorMutGenes)
# now extract the sub-matrix of mutcnaMatrix that only contain the genes that are mutated in a given tumor t
# check if special operations to create combinations of SGA events are needed. If combination operation is needed,
# new combined muation matrix will be created
if opFlag == OR:
tumorMutMatrix = createORComb(tumorMutGenes, ppiDict, mutcnaMatrix)
else: # default. Extract columns of mutcnaMatrix corresponding to the altered genes
tumorMutMatrix = mutcnaMatrix.data[:, tumormutGeneIndx]
# Include the tumor-type label into the tumorMutMatrix as a tissue-specific
# fake Gt to capture the DEGs that has tissue-specific characterisitics
if PANCANFlag:
tumorTypeLabelIndx = np.where(tumorTypeMatrix.data[t,:] == 1)[0]
if len(tumorTypeLabelIndx) != 1:
raise Exception("Fail to extract tumor type")
# add the label to the tumorMutGenes
tumorMutMatrix = np.hstack((tumorMutMatrix, tumorTypeMatrix.data[:,tumorTypeLabelIndx]))
tumorTypeName = tumorTypes[tumorTypeLabelIndx]
tumorMutGenes.append(tumorTypeName)
nTumorMutGenes = len(tumorMutGenes)
# calculate single pairwise likelihood that an SGA causes a DEG. Return a matrix where rows are mutGenes,
# columns are DEGs, currently without the joint impact
tumorLnFScore = calcF(tumorMutMatrix, tumorDEGMatrix, alphaIJKList)
# If PANCAN analysis, construct combinations of tumor-type label with different GTs to determine the
# likelihood of DEG jointly conditioning on GT and tumor-type label. This enables us to capture
# the fact that a GT regulate a GE but they also have a high tendency in co-occurring in a specific tumor type
if PANCANFlag:
if opFlag == AND:
raise Exception ("Combination of AND operation with PanCan analysis is not implemented")
# Now, calcuate the log likelihood of joint impact of tumor label with individual GTs on each GE
jointGTandTumorLableFScore = np.zeros((tumorMutMatrix.shape[1], tumorDEGMatrix.shape[1]))
# GT == 1 && Label == 1. Use mulitplication as AND operation
tmpMutMatrix = np.multiply(tumorMutMatrix, tumorTypeMatrix.data[:, tumorTypeLabelIndx])
tumorLnFScore = calcF(tmpMutMatrix, tumorDEGMatrix, alphaIJKList)
jointGTandTumorLableFScore = add(jointGTandTumorLableFScore, tumorLnFScore)
# GT == 1 && label == 0
tmpMutMatrix = np.multiply(tumorMutMatrix, tumorTypeMatrix.data[:, tumorTypeLabelIndx]==0)
tumorLnFScore = calcF(tmpMutMatrix, tumorDEGMatrix, alphaIJKList)
jointGTandTumorLableFScore = add(jointGTandTumorLableFScore, tumorLnFScore)
# GT == 0 && label == 1
tmpMutMatrix = np.multiply(tumorMutMatrix == 0, tumorTypeMatrix.data[:, tumorTypeLabelIndx])
tumorLnFScore = calcF(tmpMutMatrix, tumorDEGMatrix, alphaIJKList)
jointGTandTumorLableFScore = add(jointGTandTumorLableFScore, tumorLnFScore)
# GT == 0 && label == 0
tmpMutMatrix = np.multiply(tumorMutMatrix == 0, tumorTypeMatrix.data[:, tumorTypeLabelIndx] == 0)
tumorLnFScore = calcF(tmpMutMatrix, tumorDEGMatrix, alphaIJKList)
jointGTandTumorLableFScore = add(jointGTandTumorLableFScore, tumorLnFScore)
# stack the the joint loglikelihood matrix on top to the tumorLnFScore.
#Remove the tumor-type label variable from the matrix derived from tumorMutMatrix
tumorLnFScore = np.vstack((jointGTandTumorLableFScore[:-1,:] , tumorLnFScore))
# Calculate the likelihood that A0, which is 1 for all tumors, as a cause for DEGs.
# Then, stack to the LnFScore, equivalent to adding a column of '1' to
# represent the A0 in tumorMutMatrix
nullFscore = calcNullF(tumorDEGMatrix, alphaNull)
tumorLnFScore = np.vstack((tumorLnFScore, nullFscore))
# calcualte log of the prior probability that any of mutated genes plus A0 can be a cause for a DEG.
if PANCANFlag:
if not opFlag:
lntumorMutPriors = calcPanCanLnPrior(tumorMutGenes, dictGeneLength, vtprior[tumorTypeLabelIndx], v0)
elif opFlag == AND:
lntumorMutPriors = calcPanCanLnCombANDPrior(tumorMutGenes, dictGeneLength, vtprior[tumorTypeLabelIndx], v0)
elif opFlag == OR:
lntumorMutPriors = calcPanCanLnCombORPrior(tumorMutGenes, ppiDict, dictGeneLength, mutcnaMatrix.colnames, vtprior[tumorTypeLabelIndx], v0)
else:
if not opFlag:
lntumorMutPriors = calcLnPrior(tumorMutGenes, dictGeneLength, v0) # a m-dimension vector with m being number of mutations
else:
if opFlag == AND:
lntumorMutPriors = calcLnCombANDPrior(tumorMutGenes, dictGeneLength, v0)
elif opFlag == OR:
lntumorMutPriors = calcLnCombORPrior(tumorMutGenes, ppiDict, dictGeneLength, mutcnaMatrix.colnames, v0)
# add to each column, note double transposes because numpy broadcasts by row
tumorLnFScore = np.add(tumorLnFScore.T, lntumorMutPriors).T
# calculate the normalizer for each column (GE).
colLogSum = calcColNormalizer(tumorLnFScore)
normalizer = np.tile(colLogSum, (tumorLnFScore.shape[0], 1))
posteriorAll = np.exp(add(tumorLnFScore, - normalizer))
# now sum the posterior of each single GT with the posteriors of joint GT-Tumor-Type
posterior = np.add(posteriorAll[0:nTumorMutGenes-1, :], posteriorAll[nTumorMutGenes - 1:-2, :])
posterior = np.vstack((posterior, posteriorAll[-2:, :]))
#write out the results
tumorMutGenes.append('A0')
tumorPosterior = NamedMatrix(npMatrix = posterior, rownames = tumorMutGenes, colnames = tumorDEGGenes)
tumorPosterior.writeToText(filePath = outputPath, filename = tumorNames[t] + ".csv")
def calcTCI (mutcnaMatrixFN, degMatrixFN, alphaNull = [1, 1], alphaIJKList = [2, 1, 1, 2],
v0=0.2, ppiDict = None, dictGeneLength = None, outputPath = ".", opFlag = None, rowBegin=0, rowEnd = None):
"""
calcTCI (mutcnaMatrix, degMatrix, alphaIJList, alphaIJKList, dictGeneLength)
Calculate the causal scores between each pair of SGA and DEG observed in each tumor
Inputs:
mutcnaMatrixFN A file containing a N x G binary matrix containing the mutation and CNA
data of all tumors. N is the number of tumors and
G is number of total number of unique genes. For a
tumor, genes that have SGAs are indicated by "1"s and "0"
otherwise.
degMatrixFN A file contains a N x G' binary matrix representing DEG
status. A "1" indicate a gene is differentially expressed
in a tumor.
alphaIJList A list of Dirichlet hyperparameters defining the prior
that a mutation event occurs
alphaIJKList A list of Dirichlet hyperparameters for caulate the prior
of condition probability parameters. alphaIJK[0]: mut == 0 && deg == 0;
alphaIJK[1]: mut == 0 && deg == 1; alphaIJK[2]: mut == 1 && deg == 0;
alphaIJK[3]: mut == 1 && deg == 1
v0 A float scalar indicate the prior probability that a DEG
is caused by a non-SGA factor
ppiDict A dictionary keeps PPI network in the form an adjecency list (a dictionary of dictionary)
dictGeneLength A dictionary keeps the length of each of G genes in the
mutcnaMatrix
rowBegin, rowEnd These two arguments control allow user to choose which block out of all tumors (defined by the two
row numbers) will be processes in by this function. This can be used to process
mulitple block in a parallel fashion.
"""
# read in data in the form of NamedMatrix
try:
mutcnaMatrix = NamedMatrix(mutcnaMatrixFN)
except:
print "Failed to import data matrix %s\n" % mutcnaMatrixFN
sys.exit()
try:
degMatrix = NamedMatrix(degMatrixFN)
except:
print "Failed to import data matrix %s\n" % degMatrixFN
sys.exit()
exprsTumorNames = [x.replace("\"", "") for x in degMatrix.getRownames()]
mutTumorNames = [x.replace("\"", "") for x in mutcnaMatrix.getRownames()]
if exprsTumorNames != mutTumorNames:
print "The tumors for mutcnaMatrix and degMatrix do not fully overlap!"
print degMatrix.getRownames()
print mutcnaMatrix.getRownames()
sys.exit()
if not dictGeneLength :
print "Gene length dictionary not provided, quit\n"
sys.exit()
tumorNames = degMatrix.getRownames()
nTumors, nMutGenes = mutcnaMatrix.shape()
mutGeneNames = mutcnaMatrix.getColnames()
degGeneNames = degMatrix.getColnames()
# now we iterate through each tumor to infer the causal relationship between each
# pair of mut - deg
# loop through individual tumors and calculate the causal scores between each pair of SGA and DEG
if not rowEnd:
rowEnd = nTumors - 1
else:
if rowEnd >= nTumors:
rowEnd = nTumors - 1
elif rowEnd < rowBegin:
print "Invalid rowEnd < rowBegin arguments given."
sys.exit()
if rowBegin > rowEnd:
print "Invlid rowBegin > rowEnd argument given."
sys.exit()
print "Done with loading data, start processing tumor " + str(rowBegin)
for t in range(rowBegin, rowEnd):
#print pacifier
if t % 50 == 0:
print "Processed %s tumors" % str(t)
# collect data related to DEGs. Identify the genes that are differentially expressed in a tumor,
# then collect
degGeneIndx = [i for i, j in enumerate(degMatrix.data[t,:]) if j == 1]
tumorDEGGenes = [degGeneNames[i] for i in degGeneIndx]
nTumorDEGs = len(degGeneIndx) # corresponding to n, the number of DEGs in a given tumor
tumorDEGMatrix = degMatrix.data[:,degGeneIndx]
# collect data related to mutations
tumormutGeneIndx = [i for i, j in enumerate(mutcnaMatrix.data[t,:]) if j == 1]
if len(tumormutGeneIndx) < 2:
print tumorNames[t] + " has less than 2 mutations, skip."
continue
tumorMutGenes = [mutGeneNames[i] for i in tumormutGeneIndx]
# now extract the sub-matrix of mutcnaMatrix that only contain the genes that are mutated in a given tumor t
# check if special operations to create combinations of SGA events are needed. If combination operation is needed,
# new combined muation matrix will be created
if opFlag == AND:
tmpNamedMat = NamedMatrix(npMatrix = tumorMutMatrix, colnames = tumorMutGenes, rownames = tumorNames)
tumorNamedMatrix = createANDComb(tmpNamedMat, opFlag)
if not tumorNamedMatrix: # this tumor do not have any joint mutations that is oberved in 2% of all tumors
continue
tumorMutMatrix = tumorNamedMatrix.data
tumorMutGenes = tumorNamedMatrix.colnames
elif opFlag == OR:
tumorMutMatrix = createORComb(tumorMutGenes, ppiDict, mutcnaMatrix)
else:
tumorMutMatrix = mutcnaMatrix.data[:, tumormutGeneIndx]
## check operation options: 1) orginal, do nothing and contiue
# otherwise creat combinary matrix using the tumorMutMatrix
# createANDCombMatrix(tumorMutMatrix, operationFlag)
if not opFlag:
lntumorMutPriors = calcLnPrior(tumorMutGenes, dictGeneLength, v0) # a m-dimension vector with m being number of mutations
else:
#print tumorMutGenes[:10]
if opFlag == AND:
lntumorMutPriors = calcLnCombANDPrior(tumorMutGenes, dictGeneLength, v0)
elif opFlag == OR:
lntumorMutPriors = calcLnCombORPrior(tumorMutGenes, ppiDict, dictGeneLength, mutcnaMatrix.colnames, v0)
tumorMutGenes.append('A0')
# calculate the pairwise likelihood that an SGA causes a DEG
tumorLnFScore = calcF(tumorMutMatrix, tumorDEGMatrix, alphaIJKList)
# Calculate the likelihood of expression data conditioning on A0, and then stack to
# the LnFScore, equivalent to adding a column of '1' to represent the A0 in tumorMutMatrix
nullFscore = calcNullF(tumorDEGMatrix, alphaNull)
tumorLnFScore = np.vstack((tumorLnFScore, nullFscore)) #check out this later
# calcualte the prior probability that any of mutated genes can be a cause for a DEG,
# tile it up to make an nTumorMutGenes x nTumorDEG matrix
tumorMutPriorMatrix = np.tile(lntumorMutPriors, (nTumorDEGs, 1)).T
lnFScore = add(tumorLnFScore, tumorMutPriorMatrix)
# now we need to caclculate the normalized lnFScore so that each
columnAccumLogSum = np.zeros(nTumorDEGs)
for col in range(nTumorDEGs):
currLogSum = np.NINF
for j in range(lnFScore.shape[0]):
if lnFScore[j,col] == np.NINF:
continue
currLogSum = logSum(currLogSum, lnFScore[j,col])
columnAccumLogSum[col] = currLogSum
normalizer = np.tile(columnAccumLogSum, (lnFScore.shape[0], 1))
posterior = np.exp(add(lnFScore, - normalizer))
#write out the results
tumorPosterior = NamedMatrix(npMatrix = posterior, rownames = tumorMutGenes, colnames = tumorDEGGenes)
if "\"" in tumorNames[t]:
tumorNames[t] = tumorNames[t].replace("\"", "")
tumorPosterior.writeToText(filePath = outputPath, filename = tumorNames[t] + ".csv")
def calcTCI (mutcnaMatrixFN, degMatrixFN, alphaNull = [1, 1], alphaIJKList = [2, 1, 1, 2], v0=0.2, dictGeneLength = None, outputPath = ".", opFlag = None):
"""
calcTCI (mutcnaMatrix, degMatrix, alphaIJList, alphaIJKList, dictGeneLength)
Calculate the causal scores between each pair of SGA and DEG observed in each tumor
Inputs:
mutcnaMatrixFN A file containing a N x G binary matrix containing the mutation and CNA
data of all tumors. N is the number of tumors and
G is number of total number of unique genes. For a
tumor, genes that have SGAs are indicated by "1"s and "0"
otherwise.
degMatrixFN A file contains a N x G' binary matrix representing DEG
status. A "1" indicate a gene is differentially expressed
in a tumor.
alphaIJList A list of Dirichlet hyperparameters defining the prior
that a mutation event occurs
alphaIJKList A list of Dirichlet hyperparameters for caulate the prior
of condition probability parameters. alphaIJK[0]: mut == 0 && deg == 0;
alphaIJK[1]: mut == 0 && deg == 1; alphaIJK[2]: mut == 1 && deg == 0;
alphaIJK[3]: mut == 1 && deg == 1
v0 A float scalar indicate the prior probability that a DEG
is caused by a non-SGA factor
dictGeneLength A dictionary keeps the length of each of G genes in the
mutcnaMatrix
"""
# read in data in the form of NamedMatrix
try:
mutcnaMatrix = NamedMatrix(mutcnaMatrixFN)
except:
print "Failed to import data matrix %s\n" % mutcnaMatrixFN
sys.exit()
try:
degMatrix = NamedMatrix(degMatrixFN)
except:
print "Failed to import data matrix %s\n" % degMatrixFN
sys.exit()
if degMatrix.getRownames() != mutcnaMatrix.getRownames():
print "The tumors for mutcnaMatrix and degMatrix do not fully overlap!"
sys.exit()
if not dictGeneLength :
print "Gene length dictionary not provided, quit\n"
sys.exit()
# now we iterate through each tumor to infer the causal relationship between each
# pair of mut - deg
tumorNames = degMatrix.getRownames()
nTumors, nMutGenes = mutcnaMatrix.shape()
mutGeneNames = mutcnaMatrix.getColnames()
degGeneNames = degMatrix.getColnames()
for t in range(nTumors):
#print pacifier
if t % 50 == 0:
print "Processed %s tumors" % str(t)
# collect data related to mutations
tumormutGeneIndx = [i for i, j in enumerate(mutcnaMatrix.data[t,:]) if j == 1]
nTumorMutGenes = len(tumormutGeneIndx)
tumorMutGenes= [mutGeneNames[i] for i in tumormutGeneIndx]
#now extract the sub-matrix of mutcnaMatrix that only contain the genes that are mutated in a given tumor t
# stack a column of '1' to represent the A0. If combination operation is needed, new combined muation matrix
# will be created
tumorMutMatrix = mutcnaMatrix.data[:, tumormutGeneIndx]
if opFlag:
tmpNamedMat = NamedMatrix(npMatrix = tumorMutMatrix, colnames = tumorMutGenes, rownames = tumorNames)
tumorNamedMatrix = createComb(tmpNamedMat, opFlag)
if not tumorNamedMatrix: # this tumor do not have any joint mutations that is oberved in 2% of all tumors
continue
tumorMutGenes = tumorNamedMatrix.colnames
tumorMutMatrix = tumorNamedMatrix.data
## check operation options: 1) orginal, do nothing and contiue
# otherwise creat combinary matrix using the tumorMutMatrix
# createCombMatrix(tumorMutMatrix, operationFlag)
if not opFlag:
lntumorMutPriors = calcLnPrior(tumorMutGenes, dictGeneLength, v0) # a m-dimension vector with m being number of mutations
else:
lntumorMutPriors = calcLnCombPrior(tumorMutGenes, dictGeneLength, v0)
tumorMutGenes.append('A0')
# collect data related to DEGs
degGeneIndx = [i for i, j in enumerate(degMatrix.data[t,:]) if j == 1]
tumorDEGGenes = [degGeneNames[i] for i in degGeneIndx]
nTumorDEGs = len(degGeneIndx) # corresponding to n, the number of DEGs in a given tumor
tumorDEGMatrix = degMatrix.data[:,degGeneIndx]
# calculate pair-wise m x n matrix
tumorLnFScore = calcF(tumorMutMatrix, tumorDEGMatrix, alphaIJKList)
nullFscore = calcNullF(tumorDEGMatrix, alphaNull)
tumorLnFScore = np.vstack((tumorLnFScore, nullFscore)) #check out this later
# calcualte the prior probability that any of mutated genes can be a cause for a DEG,
# tile it up to make an nTumorMutGenes x nTumorDEG matrix
tumorMutPriorMatrix = np.tile(lntumorMutPriors, (nTumorDEGs, 1)).T
lnFScore = add(tumorLnFScore, tumorMutPriorMatrix)
#debug code below two lines
#tmpOut = NamedMatrix(npMatrix = lnFScore, colnames = tumorDEGGenes, rownames = tumorMutGenes)
#tmpOut.writeToText(outputPath, filename = tumorNames[t] + "fscore.csv")
# now we need to caclculate the normalized lnFScore so that each
columnAccumLogSum = np.zeros(nTumorDEGs)
for col in range(nTumorDEGs):
currLogSum = np.NINF
for j in range(lnFScore.shape[0]):
if lnFScore[j,col] == np.NINF:
continue
currLogSum = logSum(currLogSum, lnFScore[j,col])
columnAccumLogSum[col] = currLogSum
normalizer = np.tile(columnAccumLogSum, (lnFScore.shape[0], 1))
posterior = np.exp(add(lnFScore, - normalizer))
#write out the results
tumorPosterior = NamedMatrix(npMatrix = posterior, rownames = tumorMutGenes, colnames = tumorDEGGenes)
tumorPosterior.writeToText(outputPath, filename = tumorNames[t] + "-mut-vs-DEG-posterior.csv")
