def test_bin_distance2(self):
''' Test log2 calculation with short window. '''
maskMatrix = analyseInteraction.maskMatrix(self.inMatrix2)
maskMatrix.binDirection(maxl=1)
df = maskMatrix.binDF
self.assertTrue(self.compna(df['log2'], pd.Series(
[np.nan, 1, 0, 0])))
def test_dataframe_generation(self):
''' checking bin directionality calculation '''
maskMatrix = analyseInteraction.maskMatrix(self.inMatrix1)
maskMatrix.binDirection()
df = maskMatrix.binDF
self.assertTrue(
self.compna(
df['group'],
pd.Series([
'chr1', 'chr1', 'chr1', 'chr2', np.nan, 'chr2', 'chr2',
'chr2', 'chr3'
])))
self.assertTrue(
self.compna(
df['up'],
pd.Series([0, 0.125, 0.25, 0, np.nan, 0.125, 0.25, 0.375, 0])))
self.assertTrue(
self.compna(
df['down'],
pd.Series([0.25, 0.125, 0, 0.375, np.nan, 0.25, 0.125, 0, 0])))
self.assertTrue(
self.compna(
df['inter'],
pd.Series(
[0.625, 0.625, 0.625, 0.5, np.nan, 0.5, 0.5, 0.5, 0.875])))
self.assertTrue(
self.compna(
df['log2'],
pd.Series(
[np.nan, 0, np.nan, np.nan, np.nan, 0, 0, np.nan,
np.nan])))
def test_dataframe_generation(self):
''' checking bin directionality calculation '''
maskMatrix = analyseInteraction.maskMatrix(self.inMatrix1)
maskMatrix.binDirection()
df = maskMatrix.binDF
self.assertTrue(self.compna(df['group'], pd.Series(
['chr1', 'chr1', 'chr1', 'chr2', np.nan, 'chr2', 'chr2', 'chr2', 'chr3'])))
self.assertTrue(self.compna(df['up'], pd.Series(
[0, 0.125, 0.25, 0, np.nan, 0.125, 0.25, 0.375, 0])))
self.assertTrue(self.compna(df['down'], pd.Series(
[0.25, 0.125, 0, 0.375, np.nan, 0.25, 0.125, 0, 0])))
self.assertTrue(self.compna(df['inter'], pd.Series(
[0.625, 0.625, 0.625, 0.5, np.nan, 0.5, 0.5, 0.5, 0.875])))
self.assertTrue(self.compna(df['log2'], pd.Series(
[np.nan, 0, np.nan, np.nan, np.nan, 0, 0, np.nan, np.nan])))
' median bin count: %.0f' %(np.median(colSums))
)
# Extract combined bin data
logData += '\nCombined Samples:\n Bin Data:\n%s\n%s\n' %(
' bin number: %s' %(genomeBins.binCount),
' bins below minimum: %s' %(sum(failedBins))
)
# Save log files
for prefix in prefixList:
with open(prefix + '.matrixLog', 'w') as outFile:
outFile.write(logData)
# Normalise matrices
if sum(failedBins) < genomeBins.binCount:
# Loop through count matrices
for prefix in prefixList:
# Normalise matrix
normMatrix, biasData = interactionMatrix.normaliseMatrix(
prefix + '.countMatrix.gz', failedBins)
# Save normalised interactions
np.savetxt(prefix + '.normMatrix.gz', normMatrix, '%.6f', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
# Extract bin level data
maskMatrix = analyseInteraction.maskMatrix(prefix + '.normMatrix.gz')
maskMatrix.binDF['bias'] = biasData
maskMatrix.binDirection()
maskMatrix.binDistance()
maskMatrix.binDF.to_csv(prefix + '.binData', '\t', '')
# Extract global data
distance = maskMatrix.combinedDistance(0.1)
np.savetxt(prefix + '.dist.gz', distance, '%s', '\t')
def test_bin_distance1(self):
''' Test log2 calculation with standard window. '''
maskMatrix = analyseInteraction.maskMatrix(self.inMatrix2)
maskMatrix.binDirection()
df = maskMatrix.binDF
self.assertTrue(self.compna(df['log2'], pd.Series([np.nan, 1, -1, 0])))