def __init__(self, **kwargs):
super(PathwayMiningApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
#self.addExperimentToolBar()
self.config.set_defaults({
'/Data/MiningActive': False,
'/Data/MiningDepth': 5,
'/Data/MiningType': 'c',
'/Data/MiningRelative': False,
'/Data/MiningShared': True,
})
#t = self.getCreatedToolbar('Pathway mining', 'pathway_mining')
#miningSetup = QAction( QIcon( os.path.join( self.plugin.path, 'icon-16.png' ) ), 'Set up pathway mining \u2026', self.m)
#miningSetup.setStatusTip('Set parameters for pathway mining')
#miningSetup.triggered.connect(self.onMiningSettings)
#t.addAction(miningSetup)
self.data.add_input('input_1') # Add input slot
self.data.add_input('input_2') # Add input slot
self.data.add_input('input_3') # Add input slot
self.data.add_input('input_4') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
# Setup data consumer options
self.data.consumer_defs.extend([
DataDefinition('input_1', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
DataDefinition('input_2', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
DataDefinition('input_3', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
DataDefinition('input_4', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
])
self.addConfigPanel(PathwayMiningConfigPanel, 'Pathway Mining')
self.finalise()
def __init__(self, **kwargs):
super(MapEntityApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setItemDelegate(EntityItemDelegate())
self.data.o['output'].register_interface('as_entity_table',
EntityTableInterface)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
self.table.setModel(self.data.o['output'].as_entity_table)
# Deprecate as too slow for large mappings
#self.views.addTab(WheezyView(self), 'Entities')
t = self.getCreatedToolbar('Entity mapping', 'external-data')
import_dataAction = QAction(
QIcon(os.path.join(utils.scriptdir, 'icons', 'disk--arrow.png')),
'Load annotations from file\\u2026', self.m)
import_dataAction.setStatusTip('Load annotations from .csv. file')
import_dataAction.triggered.connect(self.onImportEntities)
t.addAction(import_dataAction)
self.addExternalDataToolbar() # Add standard source data options
self.config.set_defaults({
'map_object_type': MAP_ENTITY_ALL,
})
self._entity_mapping_table = {}
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'labels_n': (None, '>0'),
'entities_t': (None, None),
}))
self.addConfigPanel(MapEntityConfigPanel, 'Mapping')
self.finalise()
def __init__(self, **kwargs):
super(PathwayMiningApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
#self.addExperimentToolBar()
self.config.set_defaults({
'/Data/MiningActive': False,
'/Data/MiningDepth': 5,
'/Data/MiningType': 'c',
'/Data/MiningRelative': False,
'/Data/MiningShared': True,
})
#t = self.getCreatedToolbar('Pathway mining', 'pathway_mining')
#miningSetup = QAction( QIcon( os.path.join( self.plugin.path, 'icon-16.png' ) ), 'Set up pathway mining \u2026', self.m)
#miningSetup.setStatusTip('Set parameters for pathway mining')
#miningSetup.triggered.connect(self.onMiningSettings)
#t.addAction(miningSetup)
self.data.add_input('input_1') # Add input slot
self.data.add_input('input_2') # Add input slot
self.data.add_input('input_3') # Add input slot
self.data.add_input('input_4') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
# Setup data consumer options
self.data.consumer_defs.extend([
DataDefinition('input_1', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
DataDefinition('input_2', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
DataDefinition('input_3', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
DataDefinition('input_4', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
])
self.addConfigPanel(PathwayMiningConfigPanel, 'Pathway Mining')
self.finalise()
def __init__(self, **kwargs):
super(MapEntityApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setItemDelegate(EntityItemDelegate())
self.data.o['output'].register_interface('as_entity_table', EntityTableInterface)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
self.table.setModel(self.data.o['output'].as_entity_table)
# Deprecate as too slow for large mappings
#self.views.addTab(WheezyView(self), 'Entities')
t = self.getCreatedToolbar('Entity mapping', 'external-data')
import_dataAction = QAction(QIcon(os.path.join(utils.scriptdir, 'icons', 'disk--arrow.png')), 'Load annotations from file\\u2026', self.m)
import_dataAction.setStatusTip('Load annotations from .csv. file')
import_dataAction.triggered.connect(self.onImportEntities)
t.addAction(import_dataAction)
self.addExternalDataToolbar() # Add standard source data options
self.config.set_defaults({
'map_object_type': MAP_ENTITY_ALL,
})
self._entity_mapping_table = {}
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'labels_n': (None, '>0'),
'entities_t': (None, None),
})
)
self.addConfigPanel(MapEntityConfigPanel, 'Mapping')
self.finalise()
class PathwayMiningApp(ui.AnalysisApp):
def __init__(self, **kwargs):
super(PathwayMiningApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
#self.addExperimentToolBar()
self.config.set_defaults({
'/Data/MiningActive': False,
'/Data/MiningDepth': 5,
'/Data/MiningType': 'c',
'/Data/MiningRelative': False,
'/Data/MiningShared': True,
})
#t = self.getCreatedToolbar('Pathway mining', 'pathway_mining')
#miningSetup = QAction( QIcon( os.path.join( self.plugin.path, 'icon-16.png' ) ), 'Set up pathway mining \u2026', self.m)
#miningSetup.setStatusTip('Set parameters for pathway mining')
#miningSetup.triggered.connect(self.onMiningSettings)
#t.addAction(miningSetup)
self.data.add_input('input_1') # Add input slot
self.data.add_input('input_2') # Add input slot
self.data.add_input('input_3') # Add input slot
self.data.add_input('input_4') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
# Setup data consumer options
self.data.consumer_defs.extend([
DataDefinition('input_1', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
DataDefinition('input_2', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
DataDefinition('input_3', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
DataDefinition('input_4', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
}, title='Source compound, gene or protein data'),
])
self.addConfigPanel(PathwayMiningConfigPanel, 'Pathway Mining')
self.finalise()
def generate(self, input_1=None, input_2=None, input_3=None, input_4=None):
#dsi = input
# Iterate all the compounds in the current analysis
# Assign score to each of the compound's pathways
# Sum up, crop and return a list of pathway_ids to display
# Pass this in as the list to view
# + requested pathways, - excluded pathways
db = self.m.db
mining_depth = self.config.get('/Data/MiningDepth')
mining_type = self.config.get('/Data/MiningType')
pathway_scores = defaultdict(int)
for dsi in input_1, input_2, input_3, input_4:
if dsi == None:
continue
print("Mining using '%s'" % mining_type)
for n, entity in enumerate(dsi.entities[1]):
if entity == None:
continue # Skip
score = dsi.data[0, n]
#score = self.analysis[ m_id ]['score']
# 1' neighbours; 2' neighbours etc. add score
# Get a list of methods in connected reactions, add their score % to this compound
# if m_id in db.compounds.keys():
# n_compounds = [r.compounds for r in db.compounds[ m_id ].reactions ]
# print n_compounds
# n_compounds = [m for ml in n_compounds for m in ml if n_m.id in self.analysis and m.id != m_id ]
# for n_m in n_compounds:
# score += self.analysis[ n_m.id ]['score'] * 0.5
# Get the entity's pathways
pathways = entity.pathways
if pathways == []:
continue
if self.config.get('/Data/MiningShared'):
# Share the change score between the associated pathways
# this prevents compounds having undue influence
score = score / len(pathways)
for p in pathways:
mining_val = {
'c': abs(score),
'u': max(0, score),
'd': abs(min(0, score)),
'm': 1.0,
't': score,
}
pathway_scores[p] += mining_val[mining_type]
# If we're using tendency scaling; abs the scores here
if mining_type == 't':
for p, v in list(pathway_scores.items()):
pathway_scores[p] = abs(v)
# If we're pruning, then remove any pathways not in keep_pathways
if self.config.get('/Data/MiningRelative'):
print("Scaling pathway scores to pathway sizes...")
for p, v in list(pathway_scores.items()):
pathway_scores[p] = float(v) / len(p.reactions)
if not pathway_scores:
# No data
raise BaseException
# Now take the accumulated scores; and create the output
pathway_scorest = list(pathway_scores.items()) # Switch it to a dict so we can sort
pathway_scorest = [(p, v) for p, v in pathway_scorest if v > 0] # Remove any scores of 0
pathway_scorest.sort(key=lambda tup: tup[1], reverse=True) # Sort by scores (either system)
# Get top N defined by mining_depth parameter
keep_pathways = pathway_scorest[0:mining_depth]
remaining_pathways = pathway_scorest[mining_depth + 1:mining_depth + 100]
print("Mining recommended %d out of %d" % (len(keep_pathways), len(pathway_scores)))
for n, p in enumerate(keep_pathways):
print("- %d. %s [%.2f]" % (n + 1, p[0].name, p[1]))
#self.analysis['mining_ranked_remaining_pathways'] = []
#if remaining_pathways:
# print "Note: Next pathways by current scoring method are..."
# for n2,p in enumerate(remaining_pathways):
# print "- %d. %s [%.2f]" % (n+n2+1, db.pathways[ p[0] ].name, p[1])
# self.analysis['mining_ranked_remaining_pathways'].append( p[0] )
#self.analysis_suggested_pathways = [db.pathways[p[0]] for p in pathway_scorest]
dso = DataSet(size=(1, len(keep_pathways)))
dso.entities[1] = [k for k, v in keep_pathways]
dso.labels[1] = [k.name for k, v in keep_pathways]
dso.data = np.array([v for k, v in keep_pathways], ndmin=2)
dso.labels[0][0] = "Pathway mining scores"
return {'output': dso}
def onMiningSettings(self):
""" Open the mining setup dialog to define conditions, ranges, class-comparisons, etc. """
dialog = dialogMiningSettings(parent=self)
ok = dialog.exec_()
if ok:
self.config.set('/Data/MiningDepth', dialog.sb_miningDepth.value())
self.config.set('/Data/MiningType', METAPATH_MINING_TYPE_CODE[dialog.cb_miningType.currentIndex()])
self.config.set('/Data/MiningRelative', dialog.xb_miningRelative.isChecked())
self.config.set('/Data/MiningShared', dialog.xb_miningShared.isChecked())
# Update the toolbar dropdown to match
self.sb_miningDepth.setValue(dialog.sb_miningDepth.value())
def onModifyMiningDepth(self):
""" Change mine depth via toolbar spinner """
self.config.set('/Data/MiningDepth', self.sb_miningDepth.value())
def onDefineExperiment(self):
""" Open the experimental setup dialog to define conditions, ranges, class-comparisons, etc. """
dialog = dialogDefineExperiment(parent=self)
ok = dialog.exec_()
if ok:
# Regenerate the graph view
self.config.set('experiment_control', dialog.cb_control.currentText())
self.config.set('experiment_test', dialog.cb_test.currentText())
# Update toolbar to match any change caused by timecourse settings
self.update_view_callback_enabled = False # Disable to stop multiple refresh as updating the list
self.cb_control.clear()
self.cb_test.clear()
self.cb_control.addItems([dialog.cb_control.itemText(i) for i in range(dialog.cb_control.count())])
self.cb_test.addItems([dialog.cb_test.itemText(i) for i in range(dialog.cb_test.count())])
class MapEntityApp(ui.GenericApp):
def __init__(self, **kwargs):
super(MapEntityApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setItemDelegate(EntityItemDelegate())
self.data.o['output'].register_interface('as_entity_table', EntityTableInterface)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
self.table.setModel(self.data.o['output'].as_entity_table)
# Deprecate as too slow for large mappings
#self.views.addTab(WheezyView(self), 'Entities')
t = self.getCreatedToolbar('Entity mapping', 'external-data')
import_dataAction = QAction(QIcon(os.path.join(utils.scriptdir, 'icons', 'disk--arrow.png')), 'Load annotations from file\\u2026', self.m)
import_dataAction.setStatusTip('Load annotations from .csv. file')
import_dataAction.triggered.connect(self.onImportEntities)
t.addAction(import_dataAction)
self.addExternalDataToolbar() # Add standard source data options
self.config.set_defaults({
'map_object_type': MAP_ENTITY_ALL,
})
self._entity_mapping_table = {}
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'labels_n': (None, '>0'),
'entities_t': (None, None),
})
)
self.addConfigPanel(MapEntityConfigPanel, 'Mapping')
self.finalise()
def onImportEntities(self):
""" Open a data file"""
filename, _ = QFileDialog.getOpenFileName(self.m, 'Load entity mapping from file', 'Load CSV mapping for name <> identity', "All compatible files (*.csv);;Comma Separated Values (*.csv);;All files (*.*)")
if filename:
self.load_datafile(filename)
def load_datafile(self, filename):
self._entity_mapping_table = {}
# Load metabolite entities mapping from file (csv)
reader = UnicodeReader(open(filename, 'rU'), delimiter=',', dialect='excel')
# Read each row in turn, build link between items on a row (multiway map)
# If either can find an entity (via synonyms; make the link)
for row in reader:
for s in row:
if s in self.m.db.index:
e = self.m.db.index[s]
break
else:
continue # next row if we find nothing
# break to here if we do
for s in row:
self._entity_mapping_table[s] = e
self.generate()
def generate(self, input=None):
dso = self.translate(input, self.m.db)
return {'output': dso}
###### TRANSLATION to METACYC IDENTIFIERS
def translate(self, data, db):
lku = {
MAP_ENTITY_ALL: db.synrev,
MAP_ENTITY_GENE: db.synrev_by_type['gene'],
MAP_ENTITY_PROTEIN: db.synrev_by_type['protein'],
MAP_ENTITY_COMPOUND: db.synrev_by_type['compound'],
}[self.config.get('map_object_type')]
# Translate loaded data names to metabolite IDs using provided database for lookup
for n, m in enumerate(data.labels[1]):
# Match first using entity mapping table if set (allows override of defaults)
if m in self._entity_mapping_table:
data.entities[1][n] = self._entity_mapping_table[m]
# Use the internal database identities
elif m and m.lower() in lku:
data.entities[1][n] = lku[m.lower()]
#self.quantities[ transid ] = self.quantities.pop( m )
#print self.metabolites
return data
class MapEntityApp(ui.GenericApp):
def __init__(self, **kwargs):
super(MapEntityApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setItemDelegate(EntityItemDelegate())
self.data.o['output'].register_interface('as_entity_table',
EntityTableInterface)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
self.table.setModel(self.data.o['output'].as_entity_table)
# Deprecate as too slow for large mappings
#self.views.addTab(WheezyView(self), 'Entities')
t = self.getCreatedToolbar('Entity mapping', 'external-data')
import_dataAction = QAction(
QIcon(os.path.join(utils.scriptdir, 'icons', 'disk--arrow.png')),
'Load annotations from file\\u2026', self.m)
import_dataAction.setStatusTip('Load annotations from .csv. file')
import_dataAction.triggered.connect(self.onImportEntities)
t.addAction(import_dataAction)
self.addExternalDataToolbar() # Add standard source data options
self.config.set_defaults({
'map_object_type': MAP_ENTITY_ALL,
})
self._entity_mapping_table = {}
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'labels_n': (None, '>0'),
'entities_t': (None, None),
}))
self.addConfigPanel(MapEntityConfigPanel, 'Mapping')
self.finalise()
def onImportEntities(self):
""" Open a data file"""
filename, _ = QFileDialog.getOpenFileName(
self.m, 'Load entity mapping from file',
'Load CSV mapping for name <> identity',
"All compatible files (*.csv);;Comma Separated Values (*.csv);;All files (*.*)"
)
if filename:
self.load_datafile(filename)
def load_datafile(self, filename):
self._entity_mapping_table = {}
# Load metabolite entities mapping from file (csv)
reader = UnicodeReader(open(filename, 'rU'),
delimiter=',',
dialect='excel')
# Read each row in turn, build link between items on a row (multiway map)
# If either can find an entity (via synonyms; make the link)
for row in reader:
for s in row:
if s in self.m.db.index:
e = self.m.db.index[s]
break
else:
continue # next row if we find nothing
# break to here if we do
for s in row:
self._entity_mapping_table[s] = e
self.generate()
def generate(self, input=None):
dso = self.translate(input, self.m.db)
return {'output': dso}
###### TRANSLATION to METACYC IDENTIFIERS
def translate(self, data, db):
lku = {
MAP_ENTITY_ALL: db.synrev,
MAP_ENTITY_GENE: db.synrev_by_type['gene'],
MAP_ENTITY_PROTEIN: db.synrev_by_type['protein'],
MAP_ENTITY_COMPOUND: db.synrev_by_type['compound'],
}[self.config.get('map_object_type')]
# Translate loaded data names to metabolite IDs using provided database for lookup
for n, m in enumerate(data.labels[1]):
# Match first using entity mapping table if set (allows override of defaults)
if m in self._entity_mapping_table:
data.entities[1][n] = self._entity_mapping_table[m]
# Use the internal database identities
elif m and m.lower() in lku:
data.entities[1][n] = lku[m.lower()]
#self.quantities[ transid ] = self.quantities.pop( m )
#print self.metabolites
return data
class PathwayMiningApp(ui.AnalysisApp):
def __init__(self, **kwargs):
super(PathwayMiningApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
#self.addExperimentToolBar()
self.config.set_defaults({
'/Data/MiningActive': False,
'/Data/MiningDepth': 5,
'/Data/MiningType': 'c',
'/Data/MiningRelative': False,
'/Data/MiningShared': True,
})
#t = self.getCreatedToolbar('Pathway mining', 'pathway_mining')
#miningSetup = QAction( QIcon( os.path.join( self.plugin.path, 'icon-16.png' ) ), 'Set up pathway mining \u2026', self.m)
#miningSetup.setStatusTip('Set parameters for pathway mining')
#miningSetup.triggered.connect(self.onMiningSettings)
#t.addAction(miningSetup)
self.data.add_input('input_1') # Add input slot
self.data.add_input('input_2') # Add input slot
self.data.add_input('input_3') # Add input slot
self.data.add_input('input_4') # Add input slot
self.data.add_output('output')
self.table = TableView()
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(self.table, 'Table', unfocus_on_refresh=True)
# Setup data consumer options
self.data.consumer_defs.extend([
DataDefinition('input_1', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
DataDefinition('input_2', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
DataDefinition('input_3', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
DataDefinition('input_4', {
'entities_t': (None, ['Compound', 'Gene', 'Protein']),
},
title='Source compound, gene or protein data'),
])
self.addConfigPanel(PathwayMiningConfigPanel, 'Pathway Mining')
self.finalise()
def generate(self, input_1=None, input_2=None, input_3=None, input_4=None):
#dsi = input
# Iterate all the compounds in the current analysis
# Assign score to each of the compound's pathways
# Sum up, crop and return a list of pathway_ids to display
# Pass this in as the list to view
# + requested pathways, - excluded pathways
db = self.m.db
mining_depth = self.config.get('/Data/MiningDepth')
mining_type = self.config.get('/Data/MiningType')
pathway_scores = defaultdict(int)
for dsi in input_1, input_2, input_3, input_4:
if dsi == None:
continue
print("Mining using '%s'" % mining_type)
for n, entity in enumerate(dsi.entities[1]):
if entity == None:
continue # Skip
score = dsi.data[0, n]
#score = self.analysis[ m_id ]['score']
# 1' neighbours; 2' neighbours etc. add score
# Get a list of methods in connected reactions, add their score % to this compound
# if m_id in db.compounds.keys():
# n_compounds = [r.compounds for r in db.compounds[ m_id ].reactions ]
# print n_compounds
# n_compounds = [m for ml in n_compounds for m in ml if n_m.id in self.analysis and m.id != m_id ]
# for n_m in n_compounds:
# score += self.analysis[ n_m.id ]['score'] * 0.5
# Get the entity's pathways
pathways = entity.pathways
if pathways == []:
continue
if self.config.get('/Data/MiningShared'):
# Share the change score between the associated pathways
# this prevents compounds having undue influence
score = score / len(pathways)
for p in pathways:
mining_val = {
'c': abs(score),
'u': max(0, score),
'd': abs(min(0, score)),
'm': 1.0,
't': score,
}
pathway_scores[p] += mining_val[mining_type]
# If we're using tendency scaling; abs the scores here
if mining_type == 't':
for p, v in list(pathway_scores.items()):
pathway_scores[p] = abs(v)
# If we're pruning, then remove any pathways not in keep_pathways
if self.config.get('/Data/MiningRelative'):
print("Scaling pathway scores to pathway sizes...")
for p, v in list(pathway_scores.items()):
pathway_scores[p] = float(v) / len(p.reactions)
if not pathway_scores:
# No data
raise BaseException
# Now take the accumulated scores; and create the output
pathway_scorest = list(
pathway_scores.items()) # Switch it to a dict so we can sort
pathway_scorest = [(p, v) for p, v in pathway_scorest
if v > 0] # Remove any scores of 0
pathway_scorest.sort(key=lambda tup: tup[1],
reverse=True) # Sort by scores (either system)
# Get top N defined by mining_depth parameter
keep_pathways = pathway_scorest[0:mining_depth]
remaining_pathways = pathway_scorest[mining_depth + 1:mining_depth +
100]
print("Mining recommended %d out of %d" %
(len(keep_pathways), len(pathway_scores)))
for n, p in enumerate(keep_pathways):
print("- %d. %s [%.2f]" % (n + 1, p[0].name, p[1]))
#self.analysis['mining_ranked_remaining_pathways'] = []
#if remaining_pathways:
# print "Note: Next pathways by current scoring method are..."
# for n2,p in enumerate(remaining_pathways):
# print "- %d. %s [%.2f]" % (n+n2+1, db.pathways[ p[0] ].name, p[1])
# self.analysis['mining_ranked_remaining_pathways'].append( p[0] )
#self.analysis_suggested_pathways = [db.pathways[p[0]] for p in pathway_scorest]
dso = DataSet(size=(1, len(keep_pathways)))
dso.entities[1] = [k for k, v in keep_pathways]
dso.labels[1] = [k.name for k, v in keep_pathways]
dso.data = np.array([v for k, v in keep_pathways], ndmin=2)
dso.labels[0][0] = "Pathway mining scores"
return {'output': dso}
def onMiningSettings(self):
""" Open the mining setup dialog to define conditions, ranges, class-comparisons, etc. """
dialog = dialogMiningSettings(parent=self)
ok = dialog.exec_()
if ok:
self.config.set('/Data/MiningDepth', dialog.sb_miningDepth.value())
self.config.set(
'/Data/MiningType',
METAPATH_MINING_TYPE_CODE[dialog.cb_miningType.currentIndex()])
self.config.set('/Data/MiningRelative',
dialog.xb_miningRelative.isChecked())
self.config.set('/Data/MiningShared',
dialog.xb_miningShared.isChecked())
# Update the toolbar dropdown to match
self.sb_miningDepth.setValue(dialog.sb_miningDepth.value())
def onModifyMiningDepth(self):
""" Change mine depth via toolbar spinner """
self.config.set('/Data/MiningDepth', self.sb_miningDepth.value())
def onDefineExperiment(self):
""" Open the experimental setup dialog to define conditions, ranges, class-comparisons, etc. """
dialog = dialogDefineExperiment(parent=self)
ok = dialog.exec_()
if ok:
# Regenerate the graph view
self.config.set('experiment_control',
dialog.cb_control.currentText())
self.config.set('experiment_test', dialog.cb_test.currentText())
# Update toolbar to match any change caused by timecourse settings
self.update_view_callback_enabled = False # Disable to stop multiple refresh as updating the list
self.cb_control.clear()
self.cb_test.clear()
self.cb_control.addItems([
dialog.cb_control.itemText(i)
for i in range(dialog.cb_control.count())
])
self.cb_test.addItems([
dialog.cb_test.itemText(i)
for i in range(dialog.cb_test.count())
])
