def on_setDriverThreshold_clicked(self):
""" User input to change intensity threshold for masking samples from correlation calculation """
x, ok = QInputDialog.getText(
self, '',
'Enter driver (relative) intensity threshold for defining sample set for correlation calculation:'
)
# Check that correlation threshold meets requirements
try:
x = float(x)
except:
x = 100 # Bit of a hack so it fails
if not ((x < 1) & (x > 0)):
_displayMessage(
"Sample intensity threshold (relative intensity) must be a number between 0 and 1"
)
else:
self.Attributes['sampleIntensityThreshold'] = x
self.sampleIntensityThresholdText.setText(
'Driver (relative) intensity threshold for defining sample set for correlation calculation: '
+ str(self.Attributes['sampleIntensityThreshold']))
def on_setDriver_clicked(self):
""" User input to set driver feature """
x, ok = QInputDialog.getText(self, '', 'Enter '
'Feature Name'
' of driver:')
# Check that driver is in dataset
featureix = self.dataset.featureMetadata.index[
self.dataset.featureMetadata['Feature Name'] == x]
try:
temp = featureix.values
self.Attributes['driver'] = temp[0]
self.Attributes['saveName'] = self.dataset.featureMetadata.loc[
self.Attributes['driver'], 'Feature Name'].replace('/', '')
self.Attributes['recalculateCorrelation'] = True
# Check if changed and action result of change
self.updatePlot()
except:
_displayMessage(
"Driver must match an entry in the ''Feature Name'' column of the feature metadata file"
)
self.resetPlot()
def on_displayCorMap_clicked(self):
""" Create interactive (plotly) heatmap of internal correlations between all features above correationThreshold to driver """
if (self.Attributes['driver'] is not None):
if ('Set' not in self.dataset.featureMetadata):
_findStructuralSets(self)
plotHeatmap(self,
savePath=os.path.join(
self.Attributes['saveDir'],
self.Attributes['saveName'] + '_plotSetsHeatmap'))
else:
_displayMessage(
"Driver feature must be selected before plots can be displayed!"
)
def on_displayCorScatter_clicked(self):
""" Create interactive (plotly) scatter plot of driver to every other feature correlating above correationThreshold """
if (self.Attributes['driver'] is not None):
if ('Set' not in self.dataset.featureMetadata):
_findStructuralSets(self)
plotCorrelationScatter(
self,
sampleIDs='Sample ID',
savePath=os.path.join(
self.Attributes['saveDir'],
self.Attributes['saveName'] + '_plotCorrelationScatter'))
else:
_displayMessage(
"Driver feature must be selected before plots can be displayed!"
)
def on_displaySetsPlot_clicked(self):
""" Create interactive (plotly) structural sets plot """
if (self.Attributes['driver'] is not None):
if ('Set' not in self.dataset.featureMetadata):
_findStructuralSets(self)
plotScatter(
self,
colourBy='Set',
mask=self.dataset.featureMetadata['Feature Mask'],
savePath=os.path.join(
self.Attributes['saveDir'],
self.Attributes['saveName'] + '_plotStructuralSets'))
else:
_displayMessage(
"Driver feature must be selected before plots can be displayed!"
)
def on_setRANSACdegree_clicked(self):
""" User input to set degree for RANSAC fit """
x, ok = QInputDialog.getText(
self, '', 'Enter required RANSAC degree (integer > 1):')
# Check that correlation threshold meets requirements
try:
x = int(x)
except:
x = -1 # Bit of a hack so it fails
if not ((x > 1)):
_displayMessage(
"RANSAC degree must be an integer greater than zero")
else:
self.Attributes['RANSACdegree'] = x
self.setRANSACdegreeText.setText(
'RANSAC degree: ' + str(self.Attributes['RANSACdegree']))
def on_setCorrelationThreshold_clicked(self):
""" User input to change correlation threshold """
x, ok = QInputDialog.getText(self, '',
'Enter required correlation threshold:')
# Check that correlation threshold meets requirements
try:
x = float(x)
except:
x = 100 # Bit of a hack so it fails
if not ((x < 1) & (x > 0)):
_displayMessage(
"Correlation threshold must be a number in the range 0 to 1")
else:
self.Attributes['correlationThreshold'] = x
self.correlationThresholdText.setText(
'Correlation threshold ' +
str(self.Attributes['correlationThreshold']))
def on_displayCorPlot_clicked(self):
""" Create interactive (plotly) correlation to selected peak plot """
if (self.Attributes['driver'] is not None):
if self.Attributes['showAllFeatures'] == False:
mask = self.dataset.featureMetadata['Feature Mask']
else:
mask = None
plotScatter(self,
colourBy='Correlation',
mask=mask,
savePath=os.path.join(
self.Attributes['saveDir'],
self.Attributes['saveName'] + '_plotCorrelation'))
else:
_displayMessage(
"Driver feature must be selected before plots can be displayed!"
)
def on_setMzThreshold_clicked(self):
""" User input to change LC-MS m/z ppm tolerance for defining match to annotations file """
x, ok = QInputDialog.getText(
self, '', 'Enter required m/z ppm tolerance (annotation match):')
# Check that correlation threshold meets requirements
try:
x = float(x)
except:
x = -1.0 # Bit of a hack so it fails
if (x < 0):
_displayMessage(
"LC-MS m/z ppm tolerance (annotation match) must be a number greater than zero"
)
else:
self.Attributes['mzThreshold'] = x
self.mzThresholdText.setText(
'LC-MS m/z ppm threshold (annotation match): ' +
str(self.Attributes['mzThreshold']))
def on_setDriverPair_clicked(self):
""" User input to set driver-pair feature """
x, ok = QInputDialog.getText(
self, '', 'Enter '
'Feature Name'
' of driver pair:')
# Check that driver is in dataset
featureix = self.dataset.featureMetadata.index[
self.dataset.featureMetadata['Feature Name'] == x]
try:
temp = featureix.values
self.Attributes['driverPair'] = temp[0]
except:
_displayMessage(
"Driver Pair must match an entry in the ''Feature Name'' column of the feature metadata file"
)
self.resetPlot()
try:
self.Attributes['saveName'] = self.dataset.featureMetadata.loc[
self.Attributes['driver'], 'Feature Name'].replace(
'/', ''
) + ' correlation to ' + self.dataset.featureMetadata.loc[
self.Attributes['driverPair'], 'Feature Name'].replace(
'/', '')
except:
_displayMessage("Driver must be set")
self.resetPlot()
# Update plot
self.updatePlot()
def on_exportButton_clicked(self):
""" Correlating features button click - save feature list to csv """
if ('driver' in self.Attributes):
# Determine putative structural sets
if ('Set' not in self.dataset.featureMetadata):
_findStructuralSets(self)
# Set mask
if self.Attributes['showAllFeatures'] == False:
mask = self.dataset.featureMetadata['Feature Mask']
else:
mask = None
_writeOutput(self, mask)
else:
_displayMessage(
"Driver must be set before results can be exported!!")
def on_setRtThreshold_clicked(self):
""" User input to change LC-MS retention time tolerance for defining structural associations and match to annotations file """
x, ok = QInputDialog.getText(
self, '',
'Enter required retention time tolerance (structural sets):')
# Check that correlation threshold meets requirements
try:
x = float(x)
except:
x = -1.0 # Bit of a hack so it fails
if (x < 0):
_displayMessage(
"LC-MS retention time tolerance (structural sets and annotation match) must be a number greater than zero"
)
else:
self.Attributes['rtThreshold'] = x
self.rtThresholdText.setText(
'LC-MS RT threshold (structural sets and annotation match): ' +
str(self.Attributes['rtThreshold']))
def on_batchButton_clicked(self):
""" Load batch file for automatically running ISTOCSY from named drivers or between defined drivers and driver-pairs"""
# Open window to load new batch file
w = _batchDialog(batchFilePath=self.Attributes['batchFilePath'])
results = w.getResults()
# Overwrite results
self.Attributes['batchFilePath'] = results['batchFilePath']
# Load the batch file
_loadBatchFile(self)
if hasattr(self, 'dataset'):
# If Driver-Pair format
if 'DriverPair' in self.batchData:
# Copy data for final csv output
output = self.batchData.copy()
# Generate correlations and plots for every driver feature in file
for ix in self.batchData.index:
# Check that driver is in dataset
featureix = self.dataset.featureMetadata.index[
self.dataset.featureMetadata['Feature Name'] ==
self.batchData.loc[ix, 'Driver']]
try:
temp = featureix.values
self.Attributes['driver'] = temp[0]
self.Attributes[
'saveName'] = self.dataset.featureMetadata.loc[
self.Attributes['driver'],
'Feature Name'].replace('/', '')
self.Attributes['recalculateCorrelation'] = True
except:
_displayMessage(
"Driver must match an entry in the ''Feature Name'' column of the feature metadata file"
)
# If Driver-Pair format
if 'DriverPair' in self.batchData:
# Check that driver pair is in dataset
featureix = self.dataset.featureMetadata.index[
self.dataset.featureMetadata['Feature Name'] ==
self.batchData.loc[ix, 'DriverPair']]
try:
temp = featureix.values
self.Attributes['driverPair'] = temp[0]
except:
_displayMessage(
"Driver Pair must match an entry in the ''Feature Name'' column of the feature metadata file"
)
self.Attributes['saveName'] = self.dataset.featureMetadata.loc[
self.Attributes['driver'], 'Feature Name'].replace(
'/', ''
) + ' correlation to ' + self.dataset.featureMetadata.loc[
self.Attributes['driverPair'],
'Feature Name'].replace('/', '')
# Set up masks
if self.Attributes['showAllFeatures'] == False:
mask = self.dataset.featureMetadata['Feature Mask']
else:
mask = None
# Update plot
self.updatePlot()
# Calculate sets
_findStructuralSets(self)
# If Driver-All format, write output for every driver in file
if 'DriverPair' not in self.batchData:
# Write output
_writeOutput(self, mask)
# Save scatter plot coloured by correlation
plotScatter(
self,
colourBy='Correlation',
mask=mask,
savePath=os.path.join(
self.Attributes['saveDir'],
self.Attributes['saveName'] + '_plotCorrelation'),
autoOpen=False)
# If Driver-Pair format, save output for every pair to write combined file at end
else:
output.loc[
ix, 'Correlation'] = self.dataset.featureMetadata.loc[
self.Attributes['driverPair'], 'Correlation']
output.loc[ix, 'RANSAC Number of Outliers'] = sum(
self.RANSAC['outliers'].
loc[:, self.dataset.featureMetadata.loc[
self.Attributes['driverPair'], 'Feature Name']])
output.loc[
ix,
'RANSAC Intercept'] = self.dataset.featureMetadata.loc[
self.Attributes['driverPair'], 'RANSAC Intercept']
output.loc[
ix, 'RANSAC Slope'] = self.dataset.featureMetadata.loc[
self.Attributes['driverPair'], 'RANSAC Slope']
output.loc[
ix, 'RANSAC SSE'] = self.dataset.featureMetadata.loc[
self.Attributes['driverPair'], 'RANSAC SSE']
# Save scatter plot with RANSAC fit
plotCorrelationScatter(self,
sampleIDs='Sample ID',
savePath=os.path.join(
self.Attributes['saveDir'],
self.Attributes['saveName'] +
'_plotCorrelationScatter'),
autoOpen=False)
# Write output - CAZ need to save which are outliers for each pair
#_writeOutput(self, mask)
# If Driver-Pair format, export final csv
if 'DriverPair' in self.batchData:
temp = os.path.split(self.Attributes['batchFilePath'])
temp = temp[1].replace('.csv', '')
output.to_csv(os.path.join(self.Attributes['saveDir'],
temp + '_batchCorrelationResults.csv'),
encoding='utf-8')
self.resetPlot()