class OWConcordance(OWWidget):
name = "Concordance"
description = "Display the context of the word."
icon = "icons/Concordance.svg"
priority = 520
class Inputs:
corpus = Input("Corpus", Corpus)
query_word = Input("Query Word", Topic)
class Outputs:
selected_documents = Output("Selected Documents", Corpus)
concordances = Output("Concordances", Corpus)
settingsHandler = PerfectDomainContextHandler(
match_values=PerfectDomainContextHandler.MATCH_VALUES_ALL)
autocommit = Setting(True)
context_width = Setting(5)
word = ContextSetting("", exclude_metas=False)
selected_rows = Setting([], schema_only=True)
class Warning(OWWidget.Warning):
multiple_words_on_input = Msg("Multiple query words on input. "
"Only the first one is considered!")
def __init__(self):
super().__init__()
self.corpus = None # Corpus
self.n_matching = '' # Info on docs matching the word
self.n_tokens = '' # Info on tokens
self.n_types = '' # Info on types (unique tokens)
self.is_word_on_input = False
# Info attributes
info_box = gui.widgetBox(self.controlArea, 'Info')
gui.label(info_box, self, 'Tokens: %(n_tokens)s')
gui.label(info_box, self, 'Types: %(n_types)s')
gui.label(info_box, self, 'Matching: %(n_matching)s')
# Width parameter
gui.spin(self.controlArea,
self,
'context_width',
3,
10,
box=True,
label="Number of words:",
callback=self.set_width)
gui.rubber(self.controlArea)
# Search
c_box = gui.widgetBox(self.mainArea, orientation="vertical")
self.input = gui.lineEdit(c_box,
self,
'word',
orientation=Qt.Horizontal,
sizePolicy=QSizePolicy(
QSizePolicy.MinimumExpanding,
QSizePolicy.Fixed),
label='Query:',
callback=self.set_word,
callbackOnType=True)
self.input.setFocus()
# Concordances view
self.conc_view = QTableView()
self.model = ConcordanceModel()
self.conc_view.setModel(self.model)
self.conc_view.setWordWrap(False)
self.conc_view.setSelectionBehavior(QTableView.SelectRows)
self.conc_view.setSelectionModel(DocumentSelectionModel(self.model))
self.conc_view.setItemDelegate(HorizontalGridDelegate())
self.conc_view.selectionModel().selectionChanged.connect(
self.selection_changed)
self.conc_view.horizontalHeader().hide()
self.conc_view.setShowGrid(False)
self.mainArea.layout().addWidget(self.conc_view)
self.set_width()
# Auto-commit box
gui.auto_commit(self.controlArea, self, 'autocommit', 'Commit',
'Auto commit is on')
def sizeHint(self): # pragma: no cover
return QSize(600, 400)
def set_width(self):
sel = self.conc_view.selectionModel().selection()
self.model.set_width(self.context_width)
if sel:
self.conc_view.selectionModel().select(
sel,
QItemSelectionModel.SelectCurrent | QItemSelectionModel.Rows)
def selection_changed(self):
selection = self.conc_view.selectionModel().selection()
self.selected_rows = sorted(
set(cell.row() for cell in selection.indexes()))
self.commit()
def set_selection(self, selection):
if selection:
sel = QItemSelection()
for row in selection:
index = self.conc_view.model().index(row, 0)
sel.select(index, index)
self.conc_view.selectionModel().select(
sel,
QItemSelectionModel.SelectCurrent | QItemSelectionModel.Rows)
@Inputs.corpus
def set_corpus(self, data=None):
self.closeContext()
self.corpus = data
if data is None: # data removed, clear selection
self.selected_rows = []
if not self.is_word_on_input:
self.word = ""
self.openContext(self.corpus)
self.model.set_corpus(self.corpus)
self.set_word()
@Inputs.query_word
def set_word_from_input(self, topic):
self.Warning.multiple_words_on_input.clear()
if self.is_word_on_input: # word changed, clear selection
self.selected_rows = []
self.is_word_on_input = topic is not None and len(topic) > 0
self.input.setEnabled(not self.is_word_on_input)
if self.is_word_on_input:
if len(topic) > 1:
self.Warning.multiple_words_on_input()
self.word = topic.metas[0, 0]
self.set_word()
def set_word(self):
self.selected_rows = []
self.model.set_word(self.word)
self.update_widget()
self.commit()
def handleNewSignals(self):
self.set_selection(self.selected_rows)
def resize_columns(self):
col_width = (self.conc_view.width() -
self.conc_view.columnWidth(1)) / 2 - 12
self.conc_view.setColumnWidth(0, col_width)
self.conc_view.setColumnWidth(2, col_width)
def resizeEvent(self, event): # pragma: no cover
super().resizeEvent(event)
self.resize_columns()
def update_widget(self):
self.conc_view.resizeColumnToContents(1)
self.resize_columns()
self.conc_view.resizeRowsToContents()
if self.corpus is not None:
self.n_matching = '{}/{}'.format(
self.model.matching_docs() if self.word else 0,
len(self.corpus))
self.n_tokens = self.model.n_tokens
self.n_types = self.model.n_types
else:
self.n_matching = ''
self.n_tokens = ''
self.n_types = ''
def commit(self):
selected_docs = sorted(
set(self.model.word_index[row][0] for row in self.selected_rows))
concordance = self.model.get_data()
if selected_docs:
selected = self.corpus[selected_docs]
self.Outputs.selected_documents.send(selected)
else:
self.Outputs.selected_documents.send(None)
self.Outputs.concordances.send(concordance)
def send_report(self):
view = self.conc_view
model = self.conc_view.model()
self.report_items("Concordances", (
("Query", model.word),
("Tokens", model.n_tokens),
("Types", model.n_types),
("Matching", self.n_matching),
))
self.report_table(view)
class OWClusterAnalysis(OWWidget):
name = "Cluster Analysis"
description = "The widget displays differentially expressed genes that characterize the cluster, " \
"and corresponding gene terms that describe differentially expressed genes"
icon = "../widgets/icons/OWClusterAnalysis.svg"
priority = 100
class Inputs:
data_table = Input('Data', Table)
custom_sets = Input('Custom Gene Sets', Table)
class Outputs:
selected_data = Output('Selected Data', Table)
gene_scores = Output('Gene Scores', Table)
gene_set_scores = Output('Gene Set Scores', Table)
class Information(OWWidget.Information):
pass
class Warning(OWWidget.Warning):
gene_enrichment = Msg('{}, {}.')
no_selected_gene_sets = Msg(
'No gene set selected, select them from Gene Sets box.')
class Error(OWWidget.Error):
no_cluster_indicator = Msg('No cluster indicator in the input data')
gene_as_attributes = Msg(
'Genes, in the input data, are expected as column names')
organism_mismatch = Msg(
'Organism in input data and custom gene sets does not match')
cluster_batch_conflict = Msg(
'Cluster and batch must not be the same variable')
settingsHandler = ClusterAnalysisContextHandler()
cluster_indicators = ContextSetting([])
batch_indicator = ContextSetting(None)
stored_gene_sets_selection = ContextSetting(tuple())
scoring_method_selection = ContextSetting(0)
scoring_method_design = ContextSetting(0)
scoring_test_type = ContextSetting(0)
# genes filter
max_gene_count = Setting(20)
use_gene_count_filter = Setting(True)
max_gene_p_value = Setting(0.1)
use_gene_pval_filter = Setting(False)
max_gene_fdr = Setting(0.1)
use_gene_fdr_filter = Setting(True)
# gene sets filter
min_gs_count = Setting(5)
use_gs_count_filter = Setting(True)
max_gs_p_value = Setting(0.1)
use_gs_pval_filter = Setting(False)
max_gs_fdr = Setting(0.1)
use_gs_max_fdr = Setting(True)
# auto commit results
auto_commit = settings.Setting(False)
custom_gene_set_indicator = settings.Setting(None)
def __init__(self):
super().__init__()
# widget attributes
self.input_data = None
self.store_input_domain = None
self.input_genes_names = []
self.input_genes_ids = []
self.tax_id = None
self.use_attr_names = None
self.gene_id_attribute = None
# custom gene set input
self.feature_model = itemmodels.DomainModel(
valid_types=(DiscreteVariable, StringVariable))
self.custom_data = None
self.custom_tax_id = None
self.custom_use_attr_names = None
self.custom_gene_id_attribute = None
self.custom_gene_id_column = None
self.num_of_custom_sets = None
self.rows_by_cluster = None
self.rows_by_batch = None
self.clusters = []
self.new_cluster_profile = []
# data model
self.cluster_info_model = None
# Info
info_box = vBox(self.controlArea, 'Info')
self.input_info = widgetLabel(info_box)
# Cluster selection
self.cluster_indicator_model = itemmodels.DomainModel(
valid_types=(DiscreteVariable, ), separators=False)
self.cluster_indicator_box = widgetBox(self.controlArea,
'Cluster Indicator')
self.cluster_indicator_view = listView(
self.cluster_indicator_box,
self,
'cluster_indicators',
model=self.cluster_indicator_model,
selectionMode=QListWidget.MultiSelection,
callback=self.invalidate,
sizeHint=QSize(256, 70))
# Batch selection
self.batch_indicator_model = itemmodels.DomainModel(
valid_types=(DiscreteVariable, ), separators=False, placeholder="")
box = widgetBox(self.controlArea, 'Batch Indicator')
self.batch_indicator_combobox = comboBox(
box,
self,
'batch_indicator',
model=self.batch_indicator_model,
sendSelectedValue=True,
callback=self.batch_indicator_changed)
# Gene scoring
box = widgetBox(self.controlArea, 'Gene Scoring')
self.gene_scoring = GeneScoringWidget(box, self)
self.gene_scoring.set_method_selection_area('scoring_method_selection')
self.gene_scoring.set_method_design_area('scoring_method_design')
self.gene_scoring.set_test_type('scoring_test_type')
# Gene Sets widget
gene_sets_box = widgetBox(self.controlArea, "Gene Sets")
self.gs_widget = GeneSetsSelection(gene_sets_box, self,
'stored_gene_sets_selection')
self.gs_widget.hierarchy_tree_widget.itemClicked.connect(
self.__gene_sets_enrichment)
# custom gene sets area
box = vBox(self.controlArea, "Custom Gene Sets")
if self.custom_gene_set_indicator not in self.feature_model:
self.custom_gene_set_indicator = None
self.gs_label_combobox = comboBox(
box,
self,
"custom_gene_set_indicator",
sendSelectedValue=True,
model=self.feature_model,
callback=self.handle_custom_gene_sets)
self.gs_label_combobox.setDisabled(True)
# main area
splitter = QSplitter(Qt.Horizontal, self.mainArea)
self.mainArea.layout().addWidget(splitter)
genes_filter = widgetBox(splitter,
'Filter Genes',
orientation=QHBoxLayout())
spin(genes_filter,
self,
'max_gene_count',
0,
10000,
label='Count',
tooltip='Minimum genes count',
checked='use_gene_count_filter',
callback=self.filter_genes,
callbackOnReturn=True,
checkCallback=self.filter_genes)
doubleSpin(genes_filter,
self,
'max_gene_p_value',
0.0,
1.0,
0.0001,
label='p-value',
tooltip='Maximum p-value of the enrichment score',
checked='use_gene_pval_filter',
callback=self.filter_genes,
callbackOnReturn=True,
checkCallback=self.filter_genes)
doubleSpin(genes_filter,
self,
'max_gene_fdr',
0.0,
1.0,
0.0001,
label='FDR',
tooltip='Maximum false discovery rate',
checked='use_gene_fdr_filter',
callback=self.filter_genes,
callbackOnReturn=True,
checkCallback=self.filter_genes)
gene_sets_filter = widgetBox(splitter,
'Filter Gene Sets',
orientation=QHBoxLayout())
spin(gene_sets_filter,
self,
'min_gs_count',
0,
DISPLAY_GENE_SETS_COUNT,
label='Count',
tooltip='Minimum genes count',
checked='use_gs_count_filter',
callback=self.filter_gene_sets,
callbackOnReturn=True,
checkCallback=self.filter_gene_sets)
doubleSpin(gene_sets_filter,
self,
'max_gs_p_value',
0.0,
1.0,
0.0001,
label='p-value',
tooltip='Maximum p-value of the enrichment score',
checked='use_gs_pval_filter',
callback=self.filter_gene_sets,
callbackOnReturn=True,
checkCallback=self.filter_gene_sets)
doubleSpin(gene_sets_filter,
self,
'max_gs_fdr',
0.0,
1.0,
0.0001,
label='FDR',
tooltip='Maximum false discovery rate',
checked='use_gs_max_fdr',
callback=self.filter_gene_sets,
callbackOnReturn=True,
checkCallback=self.filter_gene_sets)
self.cluster_info_view = QTableView()
self.cluster_info_view.verticalHeader().setVisible(False)
self.cluster_info_view.setItemDelegate(HTMLDelegate())
self.cluster_info_view.horizontalHeader().hide()
self.cluster_info_view.horizontalHeader().setSectionResizeMode(
QHeaderView.Stretch)
auto_commit(self.controlArea,
self,
"auto_commit",
"&Commit",
box=False)
self.mainArea.layout().addWidget(self.cluster_info_view)
def sizeHint(self):
return QSize(800, 600)
def __update_info_box(self):
info_string = ''
if self.input_genes_ids:
info_string += '{} samples, {} clusters\n'.format(
self.input_data.X.shape[0],
len(self.clusters) if self.clusters else '?')
info_string += '{:,d} unique genes\n'.format(
len(self.input_genes_ids))
else:
info_string += 'No genes on input.\n'
if self.custom_data:
info_string += '{} marker genes in {} sets\n'.format(
self.custom_data.X.shape[0], self.num_of_custom_sets)
self.input_info.setText(info_string)
def __set_cluster_info_model(self):
self.cluster_info_view.setModel(None)
self.cluster_info_model = ClusterModel(self)
self.cluster_info_model.add_rows(self.clusters)
# add model to the view
self.cluster_info_view.setModel(self.cluster_info_model)
# call sizeHint function
self.cluster_info_view.resizeRowsToContents()
self.cluster_info_view.selectionModel().selectionChanged.connect(
self.commit)
def __create_temp_class_var(self):
""" See no evil !"""
cluster_indicator_name = 'Cluster indicators'
var_index_lookup = dict([(val, idx) for var in self.cluster_indicators
for idx, val in enumerate(var.values)])
row_profile = None
new_cluster_values = []
cart_prod = itertools.product(
*[cluster.values for cluster in self.cluster_indicators])
for comb in cart_prod:
new_cluster_values.append(', '.join([val for val in comb]))
self.new_cluster_profile.append(
[var_index_lookup[val] for val in comb])
row_profile_lookup = dict([(tuple(profile), indx) for indx, (
profile,
_) in enumerate(zip(self.new_cluster_profile, new_cluster_values))
])
for var in self.cluster_indicators:
if row_profile is None:
row_profile = np.asarray(
self.input_data.get_column_view(var)[0], dtype=int)
else:
row_profile = np.vstack(
(row_profile,
np.asarray(self.input_data.get_column_view(var)[0],
dtype=int)))
ca_ind = DiscreteVariable.make(
cluster_indicator_name,
values=[val for val in new_cluster_values],
ordered=True)
domain = Domain(self.input_data.domain.attributes,
self.input_data.domain.class_vars,
self.input_data.domain.metas + (ca_ind, ))
table = self.input_data.transform(domain)
table[:, ca_ind] = np.array(
[[row_profile_lookup[tuple(row_profile[:, i])]]
for i in range(row_profile.shape[1])])
self.input_data = table
return ca_ind
def __set_clusters(self):
self.clusters = []
self.new_cluster_profile = []
self.cluster_var = None
if self.cluster_indicators and self.input_data:
if isinstance(self.cluster_indicators,
list) and len(self.cluster_indicators) > 1:
self.cluster_var = self.__create_temp_class_var()
else:
self.cluster_var = self.cluster_indicators[0]
self.rows_by_cluster = np.asarray(self.input_data.get_column_view(
self.cluster_var)[0],
dtype=int)
for index, name in enumerate(self.cluster_var.values):
cluster = Cluster(name, index)
self.clusters.append(cluster)
cluster.set_genes(self.input_genes_names, self.input_genes_ids)
def __set_batch(self):
self.Error.cluster_batch_conflict.clear()
self.rows_by_batch = None
if self.batch_indicator == self.cluster_var:
self.Error.cluster_batch_conflict()
return
if self.batch_indicator and self.input_data:
self.rows_by_batch = np.asarray(self.input_data.get_column_view(
self.batch_indicator)[0],
dtype=int)
def __set_genes(self):
self.input_genes_names = []
self.input_genes_ids = []
if self.use_attr_names:
for variable in self.input_data.domain.attributes:
self.input_genes_names.append(str(variable.name))
self.input_genes_ids.append(
str(variable.attributes.get(self.gene_id_attribute,
np.nan)))
def filter_genes(self):
if self.cluster_info_model:
# filter genes
# note: after gene filter is applied, we need to recalculate gene set enrichment
self.cluster_info_model.apply_gene_filters(
self.max_gene_p_value if self.use_gene_pval_filter else None,
self.max_gene_fdr if self.use_gene_fdr_filter else None,
self.max_gene_count if self.use_gene_count_filter else None)
# recalculate gene set enrichment
self.__gene_sets_enrichment()
# call sizeHint function
self.cluster_info_view.resizeRowsToContents()
# commit changes after filter
self.commit()
def filter_gene_sets(self):
if self.cluster_info_model:
# filter gene sets
self.cluster_info_model.apply_gene_sets_filters(
self.max_gs_p_value if self.use_gs_pval_filter else None,
self.max_gs_fdr if self.use_gs_max_fdr else None,
self.min_gs_count if self.use_gs_count_filter else None)
# call sizeHint function
self.cluster_info_view.resizeRowsToContents()
def __gene_enrichment(self):
design = bool(self.gene_scoring.get_selected_desig()
) # if true cluster vs. cluster else cluster vs rest
test_type = self.gene_scoring.get_selected_test_type()
method = self.gene_scoring.get_selected_method()
try:
if method.score_function == score_hypergeometric_test:
values = set(np.unique(self.input_data.X))
if (0 not in values) or (len(values) != 2):
raise ValueError('Binary data expected (use Preprocess)')
self.cluster_info_model.score_genes(
design=design,
table_x=self.input_data.X,
rows_by_cluster=self.rows_by_cluster,
rows_by_batch=self.rows_by_batch,
method=method,
alternative=test_type)
except ValueError as e:
self.Warning.gene_enrichment(str(e), 'p-values are set to 1')
def __gene_sets_enrichment(self):
if self.input_data:
self.Warning.no_selected_gene_sets.clear()
all_sets = self.gs_widget.get_hierarchies()
selected_sets = self.gs_widget.get_hierarchies(only_selected=True)
if len(selected_sets) == 0 and len(all_sets) > 0:
self.Warning.no_selected_gene_sets()
# save setting on selected hierarchies
self.stored_gene_sets_selection = tuple(selected_sets)
ref_genes = set(self.input_genes_ids)
try:
self.cluster_info_model.gene_sets_enrichment(
self.gs_widget.gs_object, selected_sets, ref_genes)
except Exception as e:
# TODO: possible exceptions?
raise e
self.filter_gene_sets()
def invalidate(self, cluster_init=True):
if self.input_data is not None and self.tax_id is not None:
self.Warning.gene_enrichment.clear()
if self.cluster_info_model is not None:
self.cluster_info_model.cancel()
self.__set_genes()
if cluster_init:
self.__set_clusters()
self.__set_batch()
self.__set_cluster_info_model()
# note: when calling self.__gene_enrichment we calculate gse automatically.
# No need to call self.__gene_sets_enrichment here
self.__gene_enrichment()
self.__update_info_box()
def batch_indicator_changed(self):
self.invalidate(cluster_init=False)
@Inputs.data_table
def handle_input(self, data):
self.closeContext()
self.Warning.clear()
self.Error.clear()
self.input_data = None
self.store_input_domain = None
self.stored_gene_sets_selection = tuple()
self.input_genes_names = []
self.input_genes_ids = []
self.tax_id = None
self.use_attr_names = None
self.gene_id_attribute = None
self.clusters = None
self.gs_widget.clear()
self.gs_widget.clear_gene_sets()
self.cluster_info_view.setModel(None)
self.cluster_indicators = []
self.cluster_var = None
self.batch_indicator = None
self.cluster_indicator_model.set_domain(None)
self.batch_indicator_model.set_domain(None)
self.__update_info_box()
if data:
self.input_data = data
self.cluster_indicator_model.set_domain(self.input_data.domain)
self.batch_indicator_model.set_domain(self.input_data.domain)
# For Cluster Indicator do not use categorical variables that contain only one value.
self.cluster_indicator_model.wrap([
item for item in self.cluster_indicator_model
if len(item.values) > 1
])
# First value in batch indicator model is a NoneType,
# we can skip it when we validate categorical variables
self.batch_indicator_model.wrap(self.batch_indicator_model[:1] + [
item for item in self.batch_indicator_model[1:]
if len(item.values) > 1
])
self.tax_id = self.input_data.attributes.get(TAX_ID, None)
self.use_attr_names = self.input_data.attributes.get(
GENE_AS_ATTRIBUTE_NAME, None)
self.gene_id_attribute = self.input_data.attributes.get(
GENE_ID_ATTRIBUTE, None)
if not self.cluster_indicator_model:
self.Error.no_cluster_indicator()
return
elif not self.use_attr_names:
self.Error.gene_as_attributes()
return
self.openContext(self.input_data.domain)
self.gs_widget.load_gene_sets(self.tax_id)
if self.cluster_indicator_model and len(
self.cluster_indicators) < 1:
self.cluster_indicators = [self.cluster_indicator_model[0]]
if self.batch_indicator_model and self.batch_indicator is None:
self.batch_indicator = self.batch_indicator_model[0]
self.invalidate()
if self.custom_data:
self.refresh_custom_gene_sets()
self._handle_future_model()
self.handle_custom_gene_sets()
@Inputs.custom_sets
def handle_custom_input(self, data):
self.Error.clear()
self.Warning.clear()
self.closeContext()
self.custom_data = None
self.custom_tax_id = None
self.custom_use_attr_names = None
self.custom_gene_id_attribute = None
self.custom_gene_id_column = None
self.num_of_custom_sets = None
self.feature_model.set_domain(None)
if data:
self.custom_data = data
self.feature_model.set_domain(self.custom_data.domain)
self.custom_tax_id = str(
self.custom_data.attributes.get(TAX_ID, None))
self.custom_use_attr_names = self.custom_data.attributes.get(
GENE_AS_ATTRIBUTE_NAME, None)
self.custom_gene_id_attribute = self.custom_data.attributes.get(
GENE_ID_ATTRIBUTE, None)
self.custom_gene_id_column = self.custom_data.attributes.get(
GENE_ID_COLUMN, None)
self._handle_future_model()
if self.input_data:
self.openContext(self.input_data.domain)
self.gs_label_combobox.setDisabled(True)
self.refresh_custom_gene_sets()
self.handle_custom_gene_sets(select_customs_flag=True)
def __check_organism_mismatch(self):
""" Check if organisms from different inputs match.
:return: True if there is a mismatch
"""
if self.tax_id is not None and self.custom_tax_id is not None:
return self.tax_id != self.custom_tax_id
return False
def _handle_future_model(self):
if self.custom_gene_set_indicator in self.feature_model:
index = self.feature_model.indexOf(self.custom_gene_set_indicator)
self.custom_gene_set_indicator = self.feature_model[index]
else:
if self.feature_model:
self.custom_gene_set_indicator = self.feature_model[0]
else:
self.custom_gene_set_indicator = None
def handle_custom_gene_sets(self, select_customs_flag=False):
if self.custom_gene_set_indicator:
if self.custom_data is not None and self.custom_gene_id_column is not None:
if self.__check_organism_mismatch():
self.gs_label_combobox.setDisabled(True)
self.Error.organism_mismatch()
self.gs_widget.update_gs_hierarchy()
self.__gene_sets_enrichment()
return
if isinstance(self.custom_gene_set_indicator,
DiscreteVariable):
labels = self.custom_gene_set_indicator.values
gene_sets_names = [
labels[int(idx)]
for idx in self.custom_data.get_column_view(
self.custom_gene_set_indicator)[0]
]
else:
gene_sets_names, _ = self.custom_data.get_column_view(
self.custom_gene_set_indicator)
self.num_of_custom_sets = len(set(gene_sets_names))
gene_names, _ = self.custom_data.get_column_view(
self.custom_gene_id_column)
hierarchy_title = (self.custom_data.name if
self.custom_data.name else 'Custom sets', )
try:
self.gs_widget.add_custom_sets(
gene_sets_names,
gene_names,
hierarchy_title=hierarchy_title,
select_customs_flag=select_customs_flag)
except GeneSetException:
pass
self.gs_label_combobox.setDisabled(False)
else:
self.gs_widget.update_gs_hierarchy()
self.__gene_sets_enrichment()
self.__update_info_box()
def refresh_custom_gene_sets(self):
self.gs_widget.clear_custom_sets()
# self.gs_widget.update_gs_hierarchy()
def gene_scores_output(self, selected_clusters):
metas = [
StringVariable('Gene'),
StringVariable(NCBI_ID),
StringVariable('Rank'),
ContinuousVariable('Statistic score'),
ContinuousVariable('P-value'),
ContinuousVariable('FDR')
]
if len(self.new_cluster_profile):
# note: order is important
metas = self.cluster_indicators + metas
domain = Domain([], metas=metas, class_vars=self.cluster_var)
data = []
for cluster in selected_clusters:
num_of_genes = len(cluster.filtered_genes)
scores = [gene.score for gene in cluster.filtered_genes]
p_vals = [gene.p_val for gene in cluster.filtered_genes]
fdr_vals = [gene.fdr for gene in cluster.filtered_genes]
gene_names = [gene.input_name for gene in cluster.filtered_genes]
gene_ids = [gene.ncbi_id for gene in cluster.filtered_genes]
rank = rankdata(p_vals, method='min')
if len(self.new_cluster_profile):
profiles = [[cluster.index] * num_of_genes]
[
profiles.append([p] * num_of_genes)
for p in self.new_cluster_profile[cluster.index]
]
else:
profiles = [[cluster.index] * num_of_genes]
for row in zip(*profiles, gene_names, gene_ids, rank, scores,
p_vals, fdr_vals):
data.append(list(row))
out_data = Table(domain, data)
out_data.attributes[TAX_ID] = self.tax_id
out_data.attributes[GENE_AS_ATTRIBUTE_NAME] = False
out_data.attributes[GENE_ID_COLUMN] = NCBI_ID
self.Outputs.gene_scores.send(out_data)
def gene_set_scores_output(self, selected_clusters):
metas = [
StringVariable('Term'),
StringVariable('Term ID'),
StringVariable('Rank'),
ContinuousVariable('P-value'),
ContinuousVariable('FDR')
]
if len(self.new_cluster_profile):
# note: order is important
metas = self.cluster_indicators + metas
domain = Domain([], metas=metas, class_vars=self.cluster_var)
data = []
for cluster in selected_clusters:
num_of_sets = len(cluster.filtered_gene_sets)
p_vals = [gs.p_val for gs in cluster.filtered_gene_sets]
fdr_vals = [gs.fdr for gs in cluster.filtered_gene_sets]
gs_names = [gs.name for gs in cluster.filtered_gene_sets]
gs_ids = [gs.gs_id for gs in cluster.filtered_gene_sets]
rank = rankdata(p_vals, method='min')
if len(self.new_cluster_profile):
profiles = [[cluster.index] * num_of_sets]
[
profiles.append([p] * num_of_sets)
for p in self.new_cluster_profile[cluster.index]
]
else:
profiles = [[cluster.index] * num_of_sets]
for row in zip(*profiles, gs_names, gs_ids, rank, p_vals,
fdr_vals):
data.append(list(row))
self.Outputs.gene_set_scores.send(Table(domain, data))
def commit(self):
selection_model = self.cluster_info_view.selectionModel()
selected_rows = selection_model.selectedRows()
selected_clusters = []
selected_cluster_indexes = set()
selected_cluster_genes = set()
if not self.input_data or not selected_rows:
self.Outputs.selected_data.send(None)
return
for sel_row in selected_rows:
cluster = sel_row.data()
selected_clusters.append(cluster)
selected_cluster_indexes.add(cluster.index)
[
selected_cluster_genes.add(gene.ncbi_id)
for gene in cluster.filtered_genes
]
# get columns of selected clusters
selected_columns = [
column for column in self.input_data.domain.attributes
if self.gene_id_attribute in column.attributes
and str(column.attributes[
self.gene_id_attribute]) in selected_cluster_genes
]
domain = Domain(selected_columns, self.input_data.domain.class_vars,
self.input_data.domain.metas)
output_data = self.input_data.from_table(domain, self.input_data)
# get rows of selected clusters
selected_rows = [
row_index
for row_index, col_index in enumerate(self.rows_by_cluster)
if col_index in selected_cluster_indexes
]
# send to output signal
self.Outputs.selected_data.send(output_data[selected_rows])
self.gene_scores_output(selected_clusters)
self.gene_set_scores_output(selected_clusters)
class OWCreateInstance(OWWidget):
name = "Create Instance"
description = "Interactively create a data instance from sample dataset."
icon = "icons/CreateInstance.svg"
category = "Data"
keywords = ["simulator"]
priority = 4000
class Inputs:
data = Input("Data", Table)
reference = Input("Reference", Table)
class Outputs:
data = Output("Data", Table)
class Information(OWWidget.Information):
nans_removed = Msg("Variables with only missing values were "
"removed from the list.")
want_main_area = False
ACTIONS = ["median", "mean", "random", "input"]
HEADER = [["name", "Variable"], ["variable", "Value"]]
Header = namedtuple("header",
[tag for tag, _ in HEADER])(*range(len(HEADER)))
values: Dict[str, Union[float, str]] = Setting({}, schema_only=True)
append_to_data = Setting(True)
auto_commit = Setting(True)
def __init__(self):
super().__init__()
self.data: Optional[Table] = None
self.reference: Optional[Table] = None
self.filter_edit = QLineEdit(textChanged=self.__filter_edit_changed,
placeholderText="Filter...")
self.view = QTableView(sortingEnabled=True,
contextMenuPolicy=Qt.CustomContextMenu,
selectionMode=QTableView.NoSelection)
self.view.customContextMenuRequested.connect(self.__menu_requested)
self.view.setItemDelegateForColumn(self.Header.variable,
VariableDelegate(self))
self.view.verticalHeader().hide()
self.view.horizontalHeader().setStretchLastSection(True)
self.view.horizontalHeader().setMaximumSectionSize(350)
self.model = VariableItemModel(self)
self.model.setHorizontalHeaderLabels([x for _, x in self.HEADER])
self.model.dataChanged.connect(self.__table_data_changed)
self.model.dataHasNanColumn.connect(self.Information.nans_removed)
self.proxy_model = QSortFilterProxyModel()
self.proxy_model.setFilterKeyColumn(-1)
self.proxy_model.setFilterCaseSensitivity(False)
self.proxy_model.setSourceModel(self.model)
self.view.setModel(self.proxy_model)
vbox = gui.vBox(self.controlArea, box=True)
vbox.layout().addWidget(self.filter_edit)
vbox.layout().addWidget(self.view)
box = gui.hBox(vbox)
gui.rubber(box)
for name in self.ACTIONS:
gui.button(box,
self,
name.capitalize(),
lambda *args, fun=name: self._initialize_values(fun),
autoDefault=False)
gui.rubber(box)
box = gui.auto_apply(self.controlArea, self, "auto_commit")
box.button.setFixedWidth(180)
box.layout().insertStretch(0)
# pylint: disable=unnecessary-lambda
append = gui.checkBox(None,
self,
"append_to_data",
"Append this instance to input data",
callback=lambda: self.commit())
box.layout().insertWidget(0, append)
self._set_input_summary()
self._set_output_summary()
self.settingsAboutToBePacked.connect(self.pack_settings)
def __filter_edit_changed(self):
self.proxy_model.setFilterFixedString(self.filter_edit.text().strip())
def __table_data_changed(self):
self.commit()
def __menu_requested(self, point: QPoint):
index = self.view.indexAt(point)
model: QSortFilterProxyModel = index.model()
source_index = model.mapToSource(index)
menu = QMenu(self)
for action in self._create_actions(source_index):
menu.addAction(action)
menu.popup(self.view.viewport().mapToGlobal(point))
def _create_actions(self, index: QModelIndex) -> List[QAction]:
actions = []
for name in self.ACTIONS:
action = QAction(name.capitalize(), self)
action.triggered.connect(
lambda *args, fun=name: self._initialize_values(fun, [index]))
actions.append(action)
return actions
def _initialize_values(self, fun: str, indices: List[QModelIndex] = None):
cont_fun = {
"median": np.nanmedian,
"mean": np.nanmean,
"random": cont_random,
"input": np.nanmean
}.get(fun, NotImplemented)
disc_fun = {
"median": majority,
"mean": majority,
"random": disc_random,
"input": majority
}.get(fun, NotImplemented)
if not self.data or fun == "input" and not self.reference:
return
self.model.dataChanged.disconnect(self.__table_data_changed)
rows = range(self.proxy_model.rowCount()) if indices is None else \
[index.row() for index in indices]
for row in rows:
index = self.model.index(row, self.Header.variable)
variable = self.model.data(index, VariableRole)
if fun == "input":
if variable not in self.reference.domain:
continue
values = self.reference.get_column_view(variable)[0]
if variable.is_primitive():
values = values.astype(float)
if all(np.isnan(values)):
continue
else:
values = self.model.data(index, ValuesRole)
if variable.is_continuous:
value = cont_fun(values)
value = round(value, variable.number_of_decimals)
elif variable.is_discrete:
value = disc_fun(values)
elif variable.is_string:
value = ""
else:
raise NotImplementedError
self.model.setData(index, value, ValueRole)
self.model.dataChanged.connect(self.__table_data_changed)
self.commit()
@Inputs.data
def set_data(self, data: Table):
self.data = data
self._set_input_summary()
self._set_model_data()
self.unconditional_commit()
def _set_model_data(self):
self.Information.nans_removed.clear()
self.model.removeRows(0, self.model.rowCount())
if not self.data:
return
self.model.set_data(self.data, self.values)
self.values = {}
self.view.horizontalHeader().setStretchLastSection(False)
self.view.resizeColumnsToContents()
self.view.resizeRowsToContents()
self.view.horizontalHeader().setStretchLastSection(True)
@Inputs.reference
def set_reference(self, data: Table):
self.reference = data
self._set_input_summary()
def _set_input_summary(self):
n_data = len(self.data) if self.data else 0
n_refs = len(self.reference) if self.reference else 0
summary, details, kwargs = self.info.NoInput, "", {}
if self.data or self.reference:
summary = f"{self.info.format_number(n_data)}, " \
f"{self.info.format_number(n_refs)}"
data_list = [("Data", self.data), ("Reference", self.reference)]
details = format_multiple_summaries(data_list)
kwargs = {"format": Qt.RichText}
self.info.set_input_summary(summary, details, **kwargs)
def _set_output_summary(self, data: Optional[Table] = None):
if data:
summary, details = len(data), format_summary_details(data)
else:
summary, details = self.info.NoOutput, ""
self.info.set_output_summary(summary, details)
def commit(self):
output_data = None
if self.data:
output_data = self._create_data_from_values()
if self.append_to_data:
output_data = self._append_to_data(output_data)
self._set_output_summary(output_data)
self.Outputs.data.send(output_data)
def _create_data_from_values(self) -> Table:
data = Table.from_domain(self.data.domain, 1)
data.name = "created"
data.X[:] = np.nan
data.Y[:] = np.nan
for i, m in enumerate(self.data.domain.metas):
data.metas[:, i] = "" if m.is_string else np.nan
values = self._get_values()
for var_name, value in values.items():
data[:, var_name] = value
return data
def _append_to_data(self, data: Table) -> Table:
assert self.data
assert len(data) == 1
var = DiscreteVariable("Source ID", values=(self.data.name, data.name))
data = Table.concatenate([self.data, data], axis=0)
domain = Domain(data.domain.attributes, data.domain.class_vars,
data.domain.metas + (var, ))
data = data.transform(domain)
data.metas[:len(self.data), -1] = 0
data.metas[len(self.data):, -1] = 1
return data
def _get_values(self) -> Dict[str, Union[str, float]]:
values = {}
for row in range(self.model.rowCount()):
index = self.model.index(row, self.Header.variable)
values[self.model.data(index, VariableRole).name] = \
self.model.data(index, ValueRole)
return values
def send_report(self):
if not self.data:
return
self.report_domain("Input", self.data.domain)
self.report_domain("Output", self.data.domain)
items = []
values: Dict = self._get_values()
for var in self.data.domain.variables + self.data.domain.metas:
val = values.get(var.name, np.nan)
if var.is_primitive():
val = var.repr_val(val)
items.append([f"{var.name}:", val])
self.report_table("Values", items)
@staticmethod
def sizeHint():
return QSize(600, 500)
def pack_settings(self):
self.values: Dict[str, Union[str, float]] = self._get_values()
class OWConcordance(OWWidget):
name = "Concordance"
description = "Display the context of the word."
icon = "icons/Concordance.svg"
priority = 520
class Inputs:
corpus = Input("Corpus", Corpus)
query_word = Input("Query Word", Topic)
class Outputs:
selected_documents = Output("Selected Documents", Corpus)
concordances = Output("Concordances", Corpus)
settingsHandler = PerfectDomainContextHandler(
match_values = PerfectDomainContextHandler.MATCH_VALUES_ALL
)
autocommit = Setting(True)
context_width = Setting(5)
word = ContextSetting("", exclude_metas=False)
selected_rows = Setting([], schema_only=True)
class Warning(OWWidget.Warning):
multiple_words_on_input = Msg("Multiple query words on input. "
"Only the first one is considered!")
def __init__(self):
super().__init__()
self.corpus = None # Corpus
self.n_matching = '' # Info on docs matching the word
self.n_tokens = '' # Info on tokens
self.n_types = '' # Info on types (unique tokens)
self.is_word_on_input = False
# Info attributes
info_box = gui.widgetBox(self.controlArea, 'Info')
gui.label(info_box, self, 'Tokens: %(n_tokens)s')
gui.label(info_box, self, 'Types: %(n_types)s')
gui.label(info_box, self, 'Matching: %(n_matching)s')
# Width parameter
gui.spin(self.controlArea, self, 'context_width', 3, 10, box=True,
label="Number of words:", callback=self.set_width)
gui.rubber(self.controlArea)
# Search
c_box = gui.widgetBox(self.mainArea, orientation="vertical")
self.input = gui.lineEdit(
c_box, self, 'word', orientation=Qt.Horizontal,
sizePolicy=QSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.Fixed),
label='Query:', callback=self.set_word, callbackOnType=True)
self.input.setFocus()
# Concordances view
self.conc_view = QTableView()
self.model = ConcordanceModel()
self.conc_view.setModel(self.model)
self.conc_view.setWordWrap(False)
self.conc_view.setSelectionBehavior(QTableView.SelectRows)
self.conc_view.setSelectionModel(DocumentSelectionModel(self.model))
self.conc_view.setItemDelegate(HorizontalGridDelegate())
self.conc_view.selectionModel().selectionChanged.connect(self.selection_changed)
self.conc_view.horizontalHeader().hide()
self.conc_view.setShowGrid(False)
self.mainArea.layout().addWidget(self.conc_view)
self.set_width()
# Auto-commit box
gui.auto_commit(self.controlArea, self, 'autocommit', 'Commit',
'Auto commit is on')
def sizeHint(self): # pragma: no cover
return QSize(600, 400)
def set_width(self):
sel = self.conc_view.selectionModel().selection()
self.model.set_width(self.context_width)
if sel:
self.conc_view.selectionModel().select(sel,
QItemSelectionModel.SelectCurrent | QItemSelectionModel.Rows)
def selection_changed(self):
selection = self.conc_view.selectionModel().selection()
self.selected_rows = sorted(set(cell.row() for cell in selection.indexes()))
self.commit()
def set_selection(self, selection):
if selection:
sel = QItemSelection()
for row in selection:
index = self.conc_view.model().index(row, 0)
sel.select(index, index)
self.conc_view.selectionModel().select(sel,
QItemSelectionModel.SelectCurrent | QItemSelectionModel.Rows)
@Inputs.corpus
def set_corpus(self, data=None):
self.closeContext()
self.corpus = data
if data is None: # data removed, clear selection
self.selected_rows = []
if not self.is_word_on_input:
self.word = ""
self.openContext(self.corpus)
self.model.set_corpus(self.corpus)
self.set_word()
@Inputs.query_word
def set_word_from_input(self, topic):
self.Warning.multiple_words_on_input.clear()
if self.is_word_on_input: # word changed, clear selection
self.selected_rows = []
self.is_word_on_input = topic is not None and len(topic) > 0
self.input.setEnabled(not self.is_word_on_input)
if self.is_word_on_input:
if len(topic) > 1:
self.Warning.multiple_words_on_input()
self.word = topic.metas[0, 0]
self.set_word()
def set_word(self):
self.selected_rows = []
self.model.set_word(self.word)
self.update_widget()
self.commit()
def handleNewSignals(self):
self.set_selection(self.selected_rows)
def resize_columns(self):
col_width = (self.conc_view.width() -
self.conc_view.columnWidth(1)) / 2 - 12
self.conc_view.setColumnWidth(0, col_width)
self.conc_view.setColumnWidth(2, col_width)
def resizeEvent(self, event): # pragma: no cover
super().resizeEvent(event)
self.resize_columns()
def update_widget(self):
self.conc_view.resizeColumnToContents(1)
self.resize_columns()
self.conc_view.resizeRowsToContents()
if self.corpus is not None:
self.n_matching = '{}/{}'.format(
self.model.matching_docs() if self.word else 0,
len(self.corpus))
self.n_tokens = self.model.n_tokens
self.n_types = self.model.n_types
else:
self.n_matching = ''
self.n_tokens = ''
self.n_types = ''
def commit(self):
selected_docs = sorted(set(self.model.word_index[row][0]
for row in self.selected_rows))
concordance = self.model.get_data()
if selected_docs:
selected = self.corpus[selected_docs]
self.Outputs.selected_documents.send(selected)
else:
self.Outputs.selected_documents.send(None)
self.Outputs.concordances.send(concordance)
def send_report(self):
view = self.conc_view
model = self.conc_view.model()
self.report_items("Concordances", (
("Query", model.word),
("Tokens", model.n_tokens),
("Types", model.n_types),
("Matching", self.n_matching),
))
self.report_table(view)
class ExtendedTableView(QWidget):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.ow = kwargs.get('parent', None)
# set layout
layout = QVBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
self.setLayout(layout)
# set splitter
self.splitter = QSplitter()
self.splitter.setOrientation(Qt.Horizontal)
# data models
self.genes_model = None
self.info_model = None
# left side list view
self.genes_view = QTableView()
self.genes_view.horizontalHeader().hide()
self.genes_view.setItemDelegate(GeneItemDelegate())
self.genes_view.horizontalHeader().setSectionResizeMode(
QHeaderView.Stretch)
# right side list view
self.info_view = QTableView()
self.info_view.setItemDelegate(HTMLDelegate())
self.info_view.horizontalHeader().hide()
self.info_view.horizontalHeader().setSectionResizeMode(
QHeaderView.Stretch)
self.splitter.addWidget(self.genes_view)
self.splitter.addWidget(self.info_view)
# self.splitter.setStretchFactor(0, 60)
# self.splitter.setStretchFactor(1, 40)
self.layout().addWidget(self.splitter)
def set_genes_model(self, rows):
self.genes_model = GeneMatcherModel()
self.genes_model.add_rows(rows)
def get_selected_gens(self):
# return a list of QModelIndex
return self.genes_selection_model().selectedRows()
def reset_genes_model(self):
if self.genes_model:
self.genes_model.deleteLater()
self.genes_model = None
def genes_selection_model(self):
return self.genes_view.selectionModel()
def reset_info_model(self):
if self.info_model:
self.info_model.deleteLater()
self.info_model = None
self.info_view.setModel(None)
def set_info_model(self, rows):
unique, partial, unknown = range(len(self.ow.filter_labels))
if self.ow.selected_filter == unique:
# create model
self.info_model = GeneMatcherModel(show_icon=False)
# add rows
self.info_model.add_rows(rows)
# add model to the view
self.info_view.setModel(self.info_model)
# disable selection of gene info cards
self.info_view.setSelectionMode(QAbstractItemView.NoSelection)
# call sizeHint function
self.info_view.resizeRowsToContents()
else:
self.reset_info_model()
class OWConcordance(OWWidget):
name = "Concordance"
description = "Display the context of the word."
icon = "icons/Concordance.svg"
priority = 30000
inputs = [
('Corpus', Table, 'set_corpus'),
('Query Word', Topic, 'set_word_from_input'),
]
outputs = [('Selected Documents', Table, )]
autocommit = Setting(True)
context_width = Setting(5)
word = Setting("")
# TODO Set selection settings.
class Warning(OWWidget.Warning):
multiple_words_on_input = Msg("Multiple query words on input. "
"Only the first one is considered!")
def __init__(self):
super().__init__()
self.corpus = None # Corpus
self.n_documents = '' # Info on docs
self.n_matching = '' # Info on docs matching the word
self.n_tokens = '' # Info on tokens
self.n_types = '' # Info on types (unique tokens)
# Info attributes
info_box = gui.widgetBox(self.controlArea, 'Info')
gui.label(info_box, self, 'Documents: %(n_documents)s')
gui.label(info_box, self, 'Tokens: %(n_tokens)s')
gui.label(info_box, self, 'Types: %(n_types)s')
gui.label(info_box, self, 'Matching: %(n_matching)s')
# Width parameter
gui.spin(self.controlArea, self, 'context_width', 3, 10, box=True,
label="Number of words:", callback=self.set_width)
gui.rubber(self.controlArea)
# Search
c_box = gui.widgetBox(self.mainArea, orientation="vertical")
self.input = gui.lineEdit(
c_box, self, 'word', orientation=Qt.Horizontal,
sizePolicy=QSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.Fixed),
label='Query:', callback=self.set_word, callbackOnType=True)
self.input.setFocus()
# Concordances view
self.conc_view = QTableView()
self.model = ConcordanceModel()
self.conc_view.setModel(self.model)
self.conc_view.setWordWrap(False)
self.conc_view.setSelectionBehavior(QTableView.SelectRows)
self.conc_view.setSelectionModel(DocumentSelectionModel(self.model))
self.conc_view.setItemDelegate(HorizontalGridDelegate())
# connect selectionChanged to self.commit(), which will be
# updated by gui.auto_commit()
self.conc_view.selectionModel().selectionChanged.connect(lambda:
self.commit())
self.conc_view.horizontalHeader().hide()
self.conc_view.setShowGrid(False)
self.mainArea.layout().addWidget(self.conc_view)
self.set_width()
# Auto-commit box
gui.auto_commit(self.controlArea, self, 'autocommit', 'Commit',
'Auto commit is on')
def sizeHint(self): # pragma: no cover
return QSize(600, 400)
def set_width(self):
sel = self.conc_view.selectionModel().selection()
self.model.set_width(self.context_width)
if sel:
self.conc_view.selectionModel().select(sel,
QItemSelectionModel.SelectCurrent | QItemSelectionModel.Rows)
def set_corpus(self, data=None):
self.corpus = data
if data is not None and not isinstance(data, Corpus):
self.corpus = Corpus.from_table(data.domain, data)
self.model.set_corpus(self.corpus)
self.update_widget()
self.commit()
def set_word_from_input(self, topic):
self.Warning.multiple_words_on_input.clear()
have_word = topic is not None and len(topic) > 0
self.input.setEnabled(not have_word)
if have_word:
if len(topic) > 1:
self.Warning.multiple_words_on_input()
self.word = topic.metas[0, 0]
self.set_word()
def set_word(self):
self.model.set_word(self.word)
self.update_widget()
def resize_columns(self):
col_width = (self.conc_view.width() -
self.conc_view.columnWidth(1)) / 2 - 12
self.conc_view.setColumnWidth(0, col_width)
self.conc_view.setColumnWidth(2, col_width)
def resizeEvent(self, event): # pragma: no cover
super().resizeEvent(event)
self.resize_columns()
def update_widget(self):
self.conc_view.resizeColumnToContents(1)
self.resize_columns()
self.conc_view.resizeRowsToContents()
if self.corpus is not None:
self.n_documents = len(self.corpus)
self.n_matching = '{}/{}'.format(
self.model.matching_docs() if self.word else 0,
self.n_documents)
self.n_tokens = sum(map(len, self.corpus.tokens)) \
if self.corpus.has_tokens() else 'n/a'
self.n_types = len(self.corpus.dictionary) \
if self.corpus.has_tokens() else 'n/a'
else:
self.n_documents = ''
self.n_matching = ''
self.n_tokens = ''
self.n_types = ''
def commit(self):
rows = [sel_range.top() for sel_range
in self.conc_view.selectionModel().selection()]
selected_docs = sorted(set(self.model.word_index[row][0]
for row in rows))
if selected_docs:
selected = self.corpus[selected_docs]
self.send("Selected Documents", selected)
else:
self.send("Selected Documents", None)
