def dicty_mutant_gene_sets(tax_id: str):
""" Return dicty mutant phenotype gene sets from Dictybase
"""
if tax_id == '44689':
gene_sets = []
gene_matcher = GeneMatcher('44689')
for phenotype, mutants in phenotypes.phenotype_mutants().items():
phenotype = phenotype.replace(",", " ")
gene_symbols = [
phenotypes.mutant_genes(mutant)[0] for mutant in mutants
]
gene_matcher.genes = gene_symbols
genes = set()
for gene in gene_matcher.genes:
if gene.gene_id is not None:
genes.add(str(gene.gene_id))
gs = GeneSet(gs_id=phenotype,
name=phenotype,
genes=genes,
hierarchy=('Dictybase', 'Phenotypes'),
organism=tax_id,
link='')
gene_sets.append(gs)
for gs_group in GeneSets(gene_sets).split_by_hierarchy():
hierarchy = gs_group.common_hierarchy()
gs_group.to_gmt_file_format(
f'{data_path}/gene_sets/{filename(hierarchy, tax_id)}')
def omim_gene_sets(org):
""" Return gene sets from OMIM (Online Mendelian Inheritance in Man) diseses
"""
if org == '9606':
gene_matcher = GeneMatcher('9606')
genesets = []
for disease in omim.diseases():
gene_symbols = omim.disease_genes(disease)
gene_matcher.genes = gene_symbols
gene_matcher.run_matcher()
genes = []
for gene in gene_matcher.genes:
if gene.ncbi_id is not None:
genes.append(int(gene.ncbi_id))
gs = GeneSet(
gs_id=disease.id,
name=disease.name,
genes=genes,
hierarchy=('OMIM', ),
organism='9606',
link=(OMIM_LINK.format(disease.id) if disease.id else None))
genesets.append(gs)
return GeneSets(genesets)
def cytoband_gene_sets(tax_id: str) -> None:
""" Create cytoband gene sets from Stanford Microarray Database
"""
if tax_id == '9606':
download_link = 'http://statweb.stanford.edu/~tibs/GSA/cytobands-stanford.gmt'
gene_matcher = GeneMatcher('9606')
with urlopen(download_link) as stream:
data = stream.read().splitlines()
genesets = []
for band in data:
b = band.decode().split('\t')
gene_symbols = b[2:]
gene_matcher.genes = gene_symbols
genes = set()
for gene in gene_matcher.genes:
if gene.gene_id is not None:
genes.add(gene.gene_id)
genesets.append(
GeneSet(gs_id=b[0],
name=b[1],
genes=genes if b[2:] else set(),
hierarchy=('Cytobands', ),
organism='9606',
link=''))
for gs_group in GeneSets(genesets).split_by_hierarchy():
hierarchy = gs_group.common_hierarchy()
gs_group.to_gmt_file_format(
f'{data_path}/gene_sets/{filename(hierarchy, tax_id)}')
def dicty_mutant_gene_sets(org):
""" Return dicty mutant phenotype gene sets from Dictybase
"""
if org == '352472':
gene_sets = []
gene_matcher = GeneMatcher('352472')
for phenotype, mutants in dicty.phenotypes.phenotype_mutants().items():
gene_symbols = [
dicty.phenotypes.mutant_genes(mutant)[0] for mutant in mutants
]
gene_matcher.genes = gene_symbols
gene_matcher.run_matcher()
genes = []
for gene in gene_matcher.genes:
if gene.ncbi_id is not None:
genes.append(int(gene.ncbi_id))
if len(gene_symbols) != len(genes):
print(len(gene_symbols), len(genes))
gs = GeneSet(gs_id=phenotype,
name=phenotype,
genes=genes,
hierarchy=('Dictybase', 'Phenotypes'),
organism='352472',
link='')
gene_sets.append(gs)
return GeneSets(gene_sets)
def cytoband_gene_sets(org):
""" Create cytoband gene sets from Stanford Microarray Database
"""
if org == '9606':
gene_matcher = GeneMatcher('9606')
with urlopen(CYTOBAND_DOWNLOAD_LINK) as stream:
data = stream.read().splitlines()
genesets = []
for band in data:
b = band.decode().split('\t')
gene_symbols = b[2:]
gene_matcher.genes = gene_symbols
gene_matcher.run_matcher()
genes = []
for gene in gene_matcher.genes:
if gene.ncbi_id is not None:
genes.append(int(gene.ncbi_id))
genesets.append(
GeneSet(gs_id=b[0],
name=b[1],
genes=genes if b[2:] else [],
hierarchy=('Cytobands', ),
organism='9606',
link=''))
return GeneSets(genesets)
def send_to_output(self, result):
self.progress_bar.finish()
self.setStatusMessage('')
etc_json, table_name = result
# convert to table
data = etc_to_table(etc_json, bool(self.gene_as_attr_name))
# set table name
data.name = table_name
# match genes
gene_matcher = GeneMatcher(str(self.organism))
if not bool(self.gene_as_attr_name):
if 'Gene' in data.domain:
data = gene_matcher.match_table_column(
data, 'Gene', StringVariable(ENTREZ_ID))
data.attributes[GENE_ID_COLUMN] = ENTREZ_ID
else:
gene_matcher.match_table_attributes(data)
data.attributes[GENE_ID_ATTRIBUTE] = ENTREZ_ID
# add table attributes
data.attributes[TAX_ID] = str(self.organism)
data.attributes[GENE_AS_ATTRIBUTE_NAME] = bool(self.gene_as_attr_name)
# reset cache indicators
self.set_cached_indicator()
# send data to the output signal
self.Outputs.etc_data.send(data)
def __init__(self, organism, ontology=None, progress_callback=None):
self.ontology = ontology
#: A dictionary mapping a gene (gene_id) to a set of all annotations of that gene.
self.gene_annotations = defaultdict(list)
#: A dictionary mapping a GO term id to a set of annotations that are directly annotated to that term
self.term_anotations = defaultdict(list)
self.all_annotations = defaultdict(list)
self._gene_names = None
self._gene_names_dict = None
self.gene_matcher = GeneMatcher(organism)
#: A list of all :class:`AnnotationRecords` instances.
self.annotations = []
self.header = ''
self.taxid = organism
self._ontology = None
try:
path = serverfiles.localpath_download(
DOMAIN,
FILENAME_ANNOTATION.format(organism),
progress_callback=progress_callback)
except FileNotFoundError:
raise taxonomy.UnknownSpeciesIdentifier(organism)
self._parse_file(path)
def test_synonym_multiple_matches(self):
gm = GeneMatcher('9606')
gm.genes = ['HB1']
gene = gm.genes[0]
self.assertEqual(gene.input_identifier, 'HB1')
# Gene matcher should not find any unique match
self.assertEqual(gene.gene_id, None)
def reactome_gene_sets(org):
""" Prepare human pathways gene sets from reactome pathways
"""
if org == '9606':
gene_matcher = GeneMatcher('9606')
with urlopen(REACTOME_DOWNLOAD_LINK) as url:
memfile = io.BytesIO(url.read())
with ZipFile(memfile, 'r') as myzip:
f = myzip.open(REACTOME_FILE_NAME)
content = f.read().decode().splitlines()
genesets = []
for path in content:
gene_symbols = path.split('\t')[2:] if path.split(
'\t')[2:] else []
gene_matcher.genes = gene_symbols
gene_matcher.run_matcher()
genes = []
for gene in gene_matcher.genes:
if gene.ncbi_id is not None:
genes.append(int(gene.ncbi_id))
gs = GeneSet(gs_id=path.split('\t')[0],
name=path.split('\t')[0],
genes=genes,
hierarchy=('Reactome', 'Pathways'),
organism='9606',
link='')
genesets.append(gs)
return GeneSets(genesets)
def test_taxonomy_change(self):
gm = GeneMatcher('4932')
self.assertEqual(gm.tax_id, '4932')
self.assertEqual(basename(normpath(gm.gene_db_path)), '4932.sqlite')
gm.tax_id = '9606'
self.assertEqual(gm.tax_id, '9606')
self.assertEqual(basename(normpath(gm.gene_db_path)), '9606.sqlite')
def test_symbol_match_scenario(self):
gm = GeneMatcher('9606')
gm.genes = ['SCN5A']
gene = gm.genes[0]
self.assertEqual(gene.input_identifier, 'SCN5A')
self.assertEqual(gene.symbol, 'SCN5A')
self.assertEqual(gene.gene_id, '6331')
def test_match_table_attributes(self):
gm = GeneMatcher('4932')
data = Table('brown-selected.tab')
data = Table.transpose(data, feature_names_column='gene')
gm.match_table_attributes(data)
for column in data.domain.attributes:
self.assertTrue(ENTREZ_ID in column.attributes)
def test_different_input_identifier_types(self):
gm = GeneMatcher('9606')
gm.genes = ['CD4', '614535', 'HB-1Y', 'ENSG00000205426']
for gene in gm.genes:
self.assertIsNotNone(gene.description)
self.assertIsNotNone(gene.tax_id)
self.assertIsNotNone(gene.species)
self.assertIsNotNone(gene.gene_id)
def find_homologs(self, genes: List[Union[str,
Gene]]) -> List[Optional[Gene]]:
gm = GeneMatcher(self.source_tax)
gm.genes = genes
homologs = [
g.homolog_gene(taxonomy_id=self.target_tax) for g in gm.genes
]
homologs = load_gene_summary(self.target_tax, homologs)
return homologs
def test_homologs(self):
gm = GeneMatcher('9606')
gm.genes = ['920']
g = gm.genes[0]
self.assertIsNotNone(g.homologs)
self.assertTrue(len(g.homologs))
self.assertIn('10090', g.homologs)
self.assertEqual(g.homology_group_id, '513')
self.assertEqual(g.homolog_gene('10090'), '12504')
self.assertIsNone(g.homolog_gene('Unknown_taxonomy'))
def _update_gene_matcher(self):
self.gene_names_from_table()
if not self.input_genes:
self._update_info_box()
if not self.gene_matcher:
self.gene_matcher = GeneMatcher(self.get_selected_organism(),
case_insensitive=True)
self.gene_matcher.genes = self.input_genes
self.gene_matcher.organism = self.get_selected_organism()
def matchDDBids(genesDDB):
matcher = GeneMatcher(44689)
matcher.genes = genesDDB
geneNames = matcher.genes
geneInfo = dict()
for gene in geneNames:
ddb = gene.input_identifier
symbol = parseNoneStr(gene.symbol)
entrez = parseNoneStr(gene.gene_id)
description = parseNoneStr(gene.description)
geneInfo[ddb] = (symbol, entrez, description)
return geneInfo
def reactome_gene_sets(tax_id: str) -> None:
""" Prepare human pathways gene sets from reactome pathways
"""
if tax_id == '9606':
download_link = 'http://www.reactome.org/download/current/ReactomePathways.gmt.zip'
file_name = 'ReactomePathways.gmt'
detail_link = 'https://reactome.org/content/detail/{}'
gene_matcher = GeneMatcher('9606')
with urlopen(download_link) as url:
memfile = io.BytesIO(url.read())
with ZipFile(memfile, 'r') as myzip:
f = myzip.open(file_name)
content = f.read().decode().splitlines()
genesets = []
for path in content:
gene_symbols = path.split('\t')[2:] if path.split(
'\t')[2:] else []
gene_matcher.genes = gene_symbols
genes = set()
for gene in gene_matcher.genes:
if gene.gene_id is not None:
genes.add(str(gene.gene_id))
pathway = path.split('\t')[0].replace(',', ' ')
pathway_id = path.split('\t')[1].replace(',', ' ')
gs = GeneSet(gs_id=pathway_id,
name=pathway,
genes=genes,
hierarchy=('Reactome', 'pathways'),
organism='9606',
link=detail_link.format(pathway_id))
genesets.append(gs)
for gs_group in GeneSets(genesets).split_by_hierarchy():
hierarchy = gs_group.common_hierarchy()
gs_group.to_gmt_file_format(
f'{data_path}/gene_sets/{filename(hierarchy, tax_id)}')
def name_genes_entrez(gene_names: list, key_entrez: bool, organism: int = ORGANISM) -> dict:
"""
Add entrez id to each gene name
:param gene_names: Gene names (eg. from dictyBase)
:param organism: organism ID
:param key_entrez: True: Entrez IDs as keys and names as values, False: vice versa
:return: Dict of gene names and matching Entres IDs for genes that have Entrez ID
"""
entrez_names = dict()
matcher = GeneMatcher(organism)
matcher.genes = gene_names
for gene in matcher.genes:
name = gene.input_identifier
entrez = gene.gene_id
if entrez is not None:
if key_entrez:
entrez_names[entrez] = name
else:
entrez_names[name] = entrez
return entrez_names
def send_to_output(self, result):
self.progress_bar.finish()
self.setStatusMessage('')
etc_json, table_name = result
# convert to table
data = etc_to_table(etc_json, bool(self.gene_as_attr_name))
# set table name
data.name = table_name
# match genes
gene_matcher = GeneMatcher(str(self.orgnism))
if not bool(self.gene_as_attr_name):
if 'Gene' in data.domain:
gene_column = data.domain['Gene']
gene_names = data.get_column_view(gene_column)[0]
gene_matcher.genes = gene_names
gene_matcher.run_matcher()
domain_ids = Domain([], metas=[StringVariable(NCBI_ID)])
data_ids = [[str(gene.ncbi_id) if gene.ncbi_id else '?']
for gene in gene_matcher.genes]
table_ids = Table(domain_ids, data_ids)
data = Table.concatenate([data, table_ids])
data.attributes[GENE_ID_COLUMN] = NCBI_ID
else:
gene_matcher.match_table_attributes(data)
data.attributes[GENE_ID_ATTRIBUTE] = NCBI_ID
# add table attributes
data.attributes[TAX_ID] = str(self.orgnism)
data.attributes[GENE_AS_ATTRIBUTE_NAME] = bool(self.gene_as_attr_name)
# reset cache indicators
self.set_cached_indicator()
# send data to the output signal
self.Outputs.etc_data.send(data)
from orangecontrib.bioinformatics.ncbi.gene import GeneMatcher, GENE_INFO_TAGS
# specify input
organism = 9606
genes_symbols_to_match = ['HB1', 'BCKDHB', 'TWIST1']
# initialize gene matcher object
gene_matcher = GeneMatcher(organism)
gene_matcher.genes = genes_symbols_to_match
# run matching process
gene_matcher.run_matcher()
# inspect results
for gene in gene_matcher.genes:
print("\ninput name: " + gene.input_name,
"\nid from ncbi: ", gene.ncbi_id,
"\nmatch type: ", gene.type_of_match
)
if gene.ncbi_id is None and gene.possible_hits:
print('possible_hits: ', [hit.ncbi_id for hit in gene.possible_hits])
def test_match_table_column(self):
gm = GeneMatcher('4932')
data = gm.match_table_column(Table('brown-selected.tab'), 'gene')
self.assertTrue(ENTREZ_ID in data.domain)
def runner(self, state: TaskState) -> Table:
exp_type = self.data_output_options.expression_type[self.exp_type].type
exp_source = self.data_output_options.expression_sources[
self.exp_source]
proc_slug = self.data_output_options.process[self.proc_slug].slug
collection_id = self.selected_collection_id
table = self.data_table
progress_steps_download = iter(np.linspace(0, 50, 2))
def callback(i: float, status=""):
state.set_progress_value(i * 100)
if status:
state.set_status(status)
if state.is_interruption_requested():
raise Exception
if not table:
collection = self.res.get_collection_by_id(collection_id)
coll_table = resdk.tables.RNATables(
collection,
expression_source=exp_source,
expression_process_slug=proc_slug,
progress_callable=wrap_callback(callback, end=0.5),
)
species = coll_table._data[0].output['species']
sample = coll_table._samples[0]
state.set_status('Downloading ...')
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
df_exp = coll_table.exp if exp_type != 'rc' else coll_table.rc
df_exp = df_exp.rename(index=coll_table.readable_index)
df_metas = coll_table.meta
df_metas = df_metas.rename(index=coll_table.readable_index)
df_qc = None
if self.append_qc_data:
# TODO: check if there is a way to detect if collection
# table contains QC data
try:
df_qc = coll_table.qc
df_qc = df_qc.rename(index=coll_table.readable_index)
except ValueError:
pass
loop.close()
state.set_status('To data table ...')
duplicates = {
item
for item, count in Counter([
label.split('.')[1]
for label in df_metas.columns.to_list() if '.' in label
]).items() if count > 1
}
# what happens if there is more nested sections?
section_name_to_label = {
section['name']: section['label']
for section in sample.descriptor_schema.schema
}
column_labels = {}
for field_schema, fields, path in iterate_schema(
sample.descriptor, sample.descriptor_schema.schema,
path=''):
path = path[1:] # this is ugly, but cant go around it
if path not in df_metas.columns:
continue
label = field_schema['label']
section_name, field_name = path.split('.')
column_labels[path] = (
label if field_name not in duplicates else
f'{section_name_to_label[section_name]} - {label}')
df_exp = df_exp.reset_index(drop=True)
df_metas = df_metas.astype('object')
df_metas = df_metas.fillna(np.nan)
df_metas = df_metas.replace('nan', np.nan)
df_metas = df_metas.rename(columns=column_labels)
if df_qc is not None:
df_metas = pd.merge(df_metas,
df_qc,
left_index=True,
right_index=True)
xym, domain_metas = vars_from_df(df_metas)
x, _, m = xym
x_metas = np.hstack((x, m))
attrs = [ContinuousVariable(col) for col in df_exp.columns]
metas = domain_metas.attributes + domain_metas.metas
domain = Domain(attrs, metas=metas)
table = Table(domain, df_exp.to_numpy(), metas=x_metas)
state.set_progress_value(next(progress_steps_download))
state.set_status('Matching genes ...')
progress_steps_gm = iter(
np.linspace(50, 99, len(coll_table.gene_ids)))
def gm_callback():
state.set_progress_value(next(progress_steps_gm))
tax_id = species_name_to_taxid(species)
gm = GeneMatcher(tax_id, progress_callback=gm_callback)
table = gm.match_table_attributes(table, rename=True)
table.attributes[TableAnnotation.tax_id] = tax_id
table.attributes[TableAnnotation.gene_as_attr_name] = True
table.attributes[TableAnnotation.gene_id_attribute] = 'Entrez ID'
self.data_table = table
state.set_status('Normalizing ...')
table = self.normalize(table)
state.set_progress_value(100)
return table
from orangecontrib.bioinformatics.dicty import phenotypes
from orangecontrib.bioinformatics.ncbi.gene import GeneMatcher
# Count of dictyBase genes and genes with EID (involved in Orange gene sets)
dicty_annotations = 0
dicty_genes = set()
orange_annotations = 0
orange_genes = set()
empty_sets = 0
gene_matcher = GeneMatcher('44689')
for phenotype, mutants in phenotypes.phenotype_mutants().items():
gene_symbols = set(
phenotypes.mutant_genes(mutant)[0] for mutant in mutants)
dicty_annotations += len(gene_symbols)
dicty_genes.update(gene_symbols)
gene_matcher.genes = gene_symbols
N_genes_set_Orange = 0
N_genes_set_dicty = len(gene_symbols)
for gene in gene_matcher.genes:
if gene.gene_id is not None:
orange_genes.add(gene.gene_id)
N_genes_set_Orange += 1
orange_annotations += N_genes_set_Orange
if N_genes_set_Orange < 1 and N_genes_set_dicty > 0:
empty_sets += 1
print('N genes with phenotype annotations in dictyBase:', len(dicty_genes),
'and in Orange Dictybase Phenotypes:', len(orange_genes))
print(
'N of genes across gene sets (with genes being involved in multiple gene sets): dictyBase',
Gene()).homology_group_id
homologs = [
gene.gene_id
for gene in self._homologs_by_group.get(homology_group, [])
if gene.tax_id == organism
]
if len(homologs) == 1:
return homologs[0]
else:
# Is possible that find more then one gene?
return None
if __name__ == "__main__":
from orangecontrib.bioinformatics.ncbi.gene import GeneMatcher, load_gene_summary
import Orange
homology = HomoloGene()
gm = GeneMatcher('4932')
genes = Orange.data.Table("brown-selected")
gm.genes = genes
_homologs = [
homology.find_homolog(str(gene.gene_id), '9606') for gene in gm.genes
]
_homologs = load_gene_summary('9606', _homologs)
for gene, homolog in zip(gm.genes, _homologs):
print(f'{gene} ----> {homolog}')
def _on_dataready(self):
self.setEnabled(True)
self.setBlocking(False)
self.progressBarFinished(processEvents=False)
try:
data = self._datatask.result()
except urlrequest.URLError as error:
self.error(0, ("Error while connecting to the NCBI ftp server! "
"'%s'" % error))
sys.excepthook(type(error), error, getattr(error, "__traceback__"))
return
finally:
self._datatask = None
data_name = data.name
samples, _ = self.selectedSamples()
self.warning(0)
message = None
from orangecontrib.bioinformatics.ncbi.gene import GeneMatcher
gene_matcher = GeneMatcher(self.currentGds.get('taxid', ''))
if self.outputRows:
def samplesinst(ex):
out = []
for meta in data.domain.metas:
out.append((meta.name, ex[meta].value))
if data.domain.class_var.name != 'class':
out.append((data.domain.class_var.name,
ex[data.domain.class_var].value))
return out
samples = set(samples)
mask = [samples.issuperset(samplesinst(ex)) for ex in data]
data = data[numpy.array(mask, dtype=bool)]
gene_matcher.match_table_attributes(data)
if len(data) == 0:
message = "No samples with selected sample annotations."
else:
samples = set(samples)
domain = Domain(
[attr for attr in data.domain.attributes
if samples.issuperset(attr.attributes.items())],
data.domain.class_var,
data.domain.metas
)
# domain.addmetas(data.domain.getmetas())
if len(domain.attributes) == 0:
message = "No samples with selected sample annotations."
stypes = set(s[0] for s in samples)
for attr in domain.attributes:
attr.attributes = dict(
(key, value) for key, value in attr.attributes.items()
if key in stypes
)
data = Table(domain, data)
if 'gene' in data.domain:
gene_column = data.domain['gene']
gene_names = data.get_column_view(gene_column)[0]
gene_matcher.genes = gene_names
gene_matcher.run_matcher()
domain_ids = Domain([], metas=[StringVariable(NCBI_ID)])
data_ids = [[str(gene.ncbi_id) if gene.ncbi_id else '?'] for gene in gene_matcher.genes]
table_ids = Table(domain_ids, data_ids)
data = Table.concatenate([data, table_ids])
if message is not None:
self.warning(0, message)
data.attributes[TAX_ID] = self.currentGds.get('taxid', '')
data.attributes[GENE_AS_ATTRIBUTE_NAME] = bool(self.outputRows)
if not bool(self.outputRows):
data.attributes[GENE_ID_COLUMN] = NCBI_ID
else:
data.attributes[GENE_ID_ATTRIBUTE] = NCBI_ID
data.name = data_name
self.send("Expression Data", data)
model = self.treeWidget.model().sourceModel()
row = self.gds.index(self.currentGds)
model.setData(model.index(row, 0), " ", Qt.DisplayRole)
self.updateInfo()
self.selectionChanged = False
def runner(
res: ResolweAPI,
data_objects: List[Data],
options: DataOutputOptions,
exp_type: int,
proc_type: int,
input_annotation: int,
state: TaskState,
) -> Table:
data_frames = []
metadata = defaultdict(list)
def parse_sample_descriptor(sample: Sample) -> None:
general = sample.descriptor.get('general', {})
for label in SAMPLE_DESCRIPTOR_LABELS:
metadata[label].append([general.get(label, '')])
metadata['sample_name'].append([sample.name])
exp_type = file_output_field = options.expression[exp_type].type
proc_type = options.process[proc_type].type
source = options.input_annotation[input_annotation].source
species = options.input_annotation[input_annotation].species
build = options.input_annotation[input_annotation].build
# apply filters
data_objects = [obj for obj in data_objects if obj.process.type == proc_type]
data_objects = [
obj
for obj in data_objects
if obj.output['source'] == source and obj.output['species'] == species and obj.output['build'] == build
]
if exp_type != 'rc':
file_output_field = 'exp'
data_objects = [obj for obj in data_objects if obj.output['exp_type'] == exp_type]
if not data_objects:
raise ResolweDataObjectsNotFound
step, steps = 0, len(data_objects) + 3
def set_progress():
nonlocal step
step += 1
state.set_progress_value(100 * (step / steps))
state.set_status('Downloading ...')
for data_object in data_objects:
set_progress()
parse_sample_descriptor(data_object.sample)
metadata['expression_type'].append([exp_type.upper()])
response = res.get_expressions(data_object.id, data_object.output[file_output_field]['file'])
with io.BytesIO() as f:
f.write(response.content)
f.seek(0)
# expressions to data frame
df = pd.read_csv(f, sep='\t', compression='gzip')
df = df.set_index('Gene').T.reset_index(drop=True)
data_frames.append(df)
state.set_status('Concatenating samples ...')
df = pd.concat(data_frames, axis=0)
state.set_status('To data table ...')
table = table_from_frame(df)
set_progress()
state.set_status('Adding metadata ...')
metas = [StringVariable(label) for label in metadata.keys()]
domain = Domain(table.domain.attributes, table.domain.class_vars, metas)
table = table.transform(domain)
for key, value in metadata.items():
table[:, key] = value
set_progress()
state.set_status('Matching genes ...')
tax_id = species_name_to_taxid(species)
gm = GeneMatcher(tax_id)
table = gm.match_table_attributes(table, rename=True)
table.attributes[TableAnnotation.tax_id] = tax_id
table.attributes[TableAnnotation.gene_as_attr_name] = True
table.attributes[TableAnnotation.gene_id_attribute] = 'Entrez ID'
set_progress()
return table
def panglao_db(file_path: str):
file_name = 'panglao_gene_markers.tab'
reference, reference_url = 'PanglaoDB', 'https://panglaodb.se/'
with gzip.open(file_path, 'rb') as f:
content = f.read().decode('utf-8').strip()
species = 0
gene_symbol = 1
cell_type = 2
genes_by_organism = defaultdict(list)
organism_mapper = {'Mm': 'Mouse', 'Hs': 'Human'}
def _gene_function_table(desc_col: StringVariable,
gm_results: GeneMatcher):
_domain = Domain([], metas=[desc_col])
_data = [[str(gene.description) if gene.description else '']
for gene in gm_results.genes]
return Table(_domain, _data)
for line in content.split('\n'):
columns = line.split('\t')
for org in columns[species].split(' '):
if org in organism_mapper.keys():
gene_entry = [
organism_mapper[org], columns[gene_symbol],
columns[cell_type], reference, reference_url
]
genes_by_organism[organism_mapper[org]].append(gene_entry)
domain = Domain(
[],
metas=[
StringVariable('Organism'),
StringVariable('Name'),
StringVariable('Cell Type'),
StringVariable('Reference'),
StringVariable('URL'),
],
)
entrez_id_column = StringVariable('Entrez ID')
description_column = StringVariable('Function')
# construct data table for mouse
gm_mouse = GeneMatcher('10090')
mouse_table = Table(domain, genes_by_organism['Mouse'])
mouse_table = gm_mouse.match_table_column(mouse_table, 'Name',
entrez_id_column)
mouse_table = Table.concatenate(
[mouse_table,
_gene_function_table(description_column, gm_mouse)])
# construct data table for human
gm_human = GeneMatcher('9606')
human_table = Table(domain, genes_by_organism['Human'])
human_table = gm_human.match_table_column(human_table, 'Name',
entrez_id_column)
human_table = Table.concatenate(
[human_table,
_gene_function_table(description_column, gm_human)])
# return combined tables
Table.concatenate([mouse_table, human_table],
axis=0).save(f'data/marker_genes/{file_name}')
def _update_gene_matcher(self):
self.gene_names_from_table()
self.gene_matcher = GeneMatcher(self.get_selected_organism(),
auto_start=False)
self.gene_matcher.genes = self.input_genes
def Update(self):
"""
Update (recompute enriched pathways) the widget state.
"""
if not self.data:
return
self.error(0)
self.information(0)
# XXX: Check data in setData, do not even allow this to be executed if
# data has no genes
try:
genes = self.GeneNamesFromData(self.data)
except ValueError:
self.error(0, "Cannot extract gene names from input.")
genes = []
if not self.useAttrNames and any("," in gene for gene in genes):
genes = reduce(add, (split_and_strip(gene, ",")
for gene in genes),
[])
self.information(0,
"Separators detected in input gene names. "
"Assuming multiple genes per instance.")
self.queryGenes = genes
self.information(1)
reference = None
if self.useReference and self.refData:
reference = self.GeneNamesFromData(self.refData)
if not self.useAttrNames \
and any("," in gene for gene in reference):
reference = reduce(add, (split_and_strip(gene, ",")
for gene in reference),
[])
self.information(1,
"Separators detected in reference gene "
"names. Assuming multiple genes per "
"instance.")
org_code = self.SelectedOrganismCode()
from orangecontrib.bioinformatics.ncbi.gene import GeneMatcher
gm = GeneMatcher(kegg.to_taxid(org_code))
gm.genes = genes
gm.run_matcher()
mapped_genes = {gene: str(ncbi_id) for gene, ncbi_id in gm.map_input_to_ncbi().items()}
def run_enrichment(org_code, genes, reference=None, progress=None):
org = kegg.KEGGOrganism(org_code)
if reference is None:
reference = org.get_ncbi_ids()
# This is here just to keep widget working without any major changes.
# map not needed, geneMatcher will not work on widget level.
unique_genes = genes
unique_ref_genes = dict([(gene, gene) for gene in set(reference)])
taxid = kegg.to_taxid(org.org_code)
# Map the taxid back to standard 'common' taxids
# (as used by 'geneset') if applicable
r_tax_map = dict((v, k) for k, v in
kegg.KEGGGenome.TAXID_MAP.items())
if taxid in r_tax_map:
taxid = r_tax_map[taxid]
# We use the kegg pathway gene sets provided by 'geneset' for
# the enrichment calculation.
kegg_api = kegg.api.CachedKeggApi()
linkmap = kegg_api.link(org.org_code, "pathway")
converted_ids = kegg_api.conv(org.org_code, 'ncbi-geneid')
kegg_sets = relation_list_to_multimap(linkmap, dict((gene.upper(), ncbi.split(':')[-1])
for ncbi, gene in converted_ids))
kegg_sets = geneset.GeneSets(input=kegg_sets)
pathways = pathway_enrichment(
kegg_sets, unique_genes.values(),
unique_ref_genes.keys(),
callback=progress
)
# Ensure that pathway entries are pre-cached for later use in the
# list/tree view
kegg_pathways = kegg.KEGGPathways()
kegg_pathways.pre_cache(
pathways.keys(), progress_callback=progress
)
return pathways, org, unique_genes, unique_ref_genes
self.progressBarInit()
self.setEnabled(False)
self.infoLabel.setText("Retrieving...\n")
progress = concurrent.methodinvoke(self, "setProgress", (float,))
self._enrichTask = concurrent.Task(
function=lambda:
run_enrichment(org_code, mapped_genes, reference, progress)
)
self._enrichTask.finished.connect(self._onEnrichTaskFinished)
self._executor.submit(self._enrichTask)
