def loadData(matrixFile, outDir):
#open the file and parse the contents
logging.info('Parsing datamatrix')
file = open(matrixFile, 'r')
#store a few things for later
headers = []
sampleSummary = Sample('all')
samples = {}
#loop over the file
for line in file.readlines():
#remove the return
line = line.rstrip('\n')
#tokeninze the line
tokens = line.split('\t')
#if it is the first line we can just store the headers
if line.startswith('GeneName'):
headers = tokens
#else we need to process the data
else:
sampleName = tokens[0]
#print sampleName
#create my sample
sample = Sample(sampleName)
#now lets start processing everything
for num, variantInfo in enumerate(tokens[1:]):
#grab the header info (we shouldn't be doing this for each sample, need to fix)
gene, refpos = headers[num + 1].split('_')
reference, position = re.findall(r"[^\W\d_]+|\d+", refpos)
#print '%i %s %s %s' % (num, gene, reference, position)
#now lets parse the variant info
#if LOW_DEPTH, then skip
if not variantInfo.startswith('LOW_DEPTH'):
variantInfoTokens = re.findall(r"[-+]?\d*\.\d+|\d+|\w+", variantInfo) #variantInfo.split('/')
#simple case is where there is just one alternate allele
if len(variantInfoTokens) == 3:
#base case
#reference
sample.addVariant(gene, reference, reference, position, 1.0 - float(variantInfoTokens[2]))
sampleSummary.addVariant(gene, reference, reference, position, 1.0 - float(variantInfoTokens[2]))
#variant
sample.addVariant(gene, reference, variantInfoTokens[1], position, float(variantInfoTokens[2]))
sampleSummary.addVariant(gene, reference, variantInfoTokens[1], position, float(variantInfoTokens[2]))
elif len(variantInfoTokens) == 6:
#need to handle when there are two alternate alleles
#reference
sample.addVariant(gene, reference, reference, position, 1.0 - float(variantInfoTokens[4]) - float(variantInfoTokens[5]))
sampleSummary.addVariant(gene, reference, reference, position, 1.0 - float(variantInfoTokens[4]) - float(variantInfoTokens[5]))
#variant 1
sample.addVariant(gene, reference, variantInfoTokens[1], position, float(variantInfoTokens[4]))
sampleSummary.addVariant(gene, reference, variantInfoTokens[1], position, float(variantInfoTokens[4]))
#variant 2
sample.addVariant(gene, reference, variantInfoTokens[2], position, float(variantInfoTokens[5]))
sampleSummary.addVariant(gene, reference, variantInfoTokens[2], position, float(variantInfoTokens[5]))
elif len(variantInfoTokens) == 2:
#this means there was a deletion
#reference
sample.addVariant(gene, reference, reference, position, 1.0 - float(variantInfoTokens[1]))
sampleSummary.addVariant(gene, reference, reference, position, 1.0 - float(variantInfoTokens[1]))
#deletion
sample.addVariant(gene, reference, '-', position, float(variantInfoTokens[1]))
sampleSummary.addVariant(gene, reference, '-', position, float(variantInfoTokens[1]))
else:
#this really shoudln't happen
print variantInfoTokens
logging.warning('Sample with more than two alternate alleles - %s %s' % (sampleName, variantInfo))
#now add the sample
samples[sampleName] = sample
#close the file
file.close()
#now lets build our feature vectors, for now it will be all the variants and all the genes
logging.info('Building feature vectors')
#dump the array to a spreadsheet for debugging
spreadsheet = xlsxwriter.Workbook('%s/features.xlsx' % outDir)
featureWorksheet = spreadsheet.add_worksheet('Features')
#set up our huge feature array and class label vectors. features is
features = np.zeros((len(samples), len(sampleSummary.getVariants()) + len(sampleSummary.getGenes())))
labels = np.zeros((len(samples)))
#now let's populate our matrix
for sampleNumber, sample in enumerate(sorted(samples)):
sample = samples[sample]
labels[sampleNumber] = sample.getClassLabel()
featureWorksheet.write(sampleNumber+1, 0, sample.getSampleName())
featureWorksheet.write(sampleNumber+1, 1, sample.getClassLabel())
#first start with the variants
for featureNumber, variant in enumerate(sorted(sampleSummary.getVariants())):
geneName, position, referenceBase, variantBase = variant.split('_')
featureWorksheet.write(0, featureNumber+2, variant)
if(sample.hasVariant(geneName, referenceBase, variantBase, position)):
#add frequency to array
features[sampleNumber][featureNumber] = sample.getVariantFrequency(geneName, referenceBase, variantBase, position)
featureWorksheet.write(sampleNumber+1, featureNumber+2, sample.getVariantFrequency(geneName, referenceBase, variantBase, position))
else:
featureWorksheet.write(sampleNumber+1, featureNumber+2, 0)
#now at the gene level
for featureNumber, gene in enumerate(sorted(sampleSummary.getGenes())):
featureWorksheet.write(0, featureNumber+len(sampleSummary.getVariants())+1, gene)
if(sample.hasGene(gene)):
features[sampleNumber][featureNumber + len(sampleSummary.getVariants())] = 1
featureWorksheet.write(sampleNumber+1, featureNumber+len(sampleSummary.getVariants())+2, 1)
else:
featureWorksheet.write(sampleNumber+1, featureNumber+len(sampleSummary.getVariants())+2, 0)
#close the spreadsheet
spreadsheet.close()
#return the matrix
print features
print labels
return(features, labels)
