def read_all_genes(disease_name=None, ensg_number=None, gene_symbol=None, gene_type=None, pValue=0.05,
pValueDirection="<", mscor=None, mscorDirection="<", correlation=None, correlationDirection="<",
sorting=None, descending=True, limit=100, offset=0, information=True):
"""
This function responds to a request for /sponge/ceRNAInteraction/findAll
and returns all interactions the given identification (ensg_number or gene_symbol) in all available datasets is in involved
:param disease_name: disease_name of interest
:param ensg_number: esng number of the gene of interest
:param gene_symbol: gene symbol of the gene of interest
:param gene_type: defines the type of gene of interest
:param pValue: pValue cutoff
:param pValueDirection: < or >
:param mscor mscor cutofff
:param mscorDirection: < or >
:param correlation: correlation cutoff
:param correlationDirection: < or >
:param sorting: how the results of the db query should be sorted
:param descending: should the results be sorted in descending or ascending order
:param limit: number of results that shouls be shown
:param offset: startpoint from where results should be shown
:param information: defines if each gene should contain all available information or not (default: True, if False: just ensg_nr will be shown)
:return: all interactions given gene is involved
"""
# test limit
if limit > 1000:
abort(404, "Limit is to high. For a high number of needed interactions please use the download section.")
# test if just one of the possible identifiers is given
if ensg_number is not None and (gene_symbol is not None or gene_type is not None) or (
gene_symbol is not None and gene_type is not None):
abort(404,
"More than one identifikation paramter is given. Please choose one out of (ensg number, gene symbol or gene type)")
queries_1 = []
queries_2 = []
# if specific disease_name is given:
if disease_name is not None:
run = models.Run.query.join(models.Dataset, models.Dataset.dataset_ID == models.Run.dataset_ID) \
.filter(models.Dataset.disease_name.like("%" + disease_name + "%")) \
.all()
if len(run) > 0:
run_IDs = [i.run_ID for i in run]
queries_1.append(models.GeneInteraction.run_ID.in_(run_IDs))
queries_2.append(models.GeneInteraction.run_ID.in_(run_IDs))
else:
abort(404, "No dataset with given disease_name found")
gene = []
# if ensg_numer is given to specify gene(s), get the intern gene_ID(primary_key) for requested ensg_nr(gene_ID)
if ensg_number is not None:
gene = models.Gene.query \
.filter(models.Gene.ensg_number.in_(ensg_number)) \
.all()
# if gene_symbol is given to specify gene(s), get the intern gene_ID(primary_key) for requested gene_symbol(gene_ID)
elif gene_symbol is not None:
gene = models.Gene.query \
.filter(models.Gene.gene_symbol.in_(gene_symbol)) \
.all()
elif gene_type is not None:
gene = models.Gene.query \
.filter(models.Gene.gene_type == gene_type) \
.all()
# save all needed queries to get correct results
if ensg_number is not None or gene_symbol is not None or gene_type is not None:
if len(gene) > 0:
gene_IDs = [i.gene_ID for i in gene]
queries_1.append(models.GeneInteraction.gene_ID1.in_(gene_IDs))
queries_2.append(models.GeneInteraction.gene_ID2.in_(gene_IDs))
else:
abort(404, "Not gene found for given ensg_number(s) or gene_symbol(s)")
# filter further depending on given statistics cutoffs
if pValue is not None:
if pValueDirection == "<":
queries_1.append(models.GeneInteraction.p_value <= pValue)
queries_2.append(models.GeneInteraction.p_value <= pValue)
else:
queries_1.append(models.GeneInteraction.p_value >= pValue)
queries_2.append(models.GeneInteraction.p_value >= pValue)
if mscor is not None:
if mscorDirection == "<":
queries_1.append(models.GeneInteraction.mscor <= mscor)
queries_2.append(models.GeneInteraction.mscor <= mscor)
else:
queries_1.append(models.GeneInteraction.mscor >= mscor)
queries_2.append(models.GeneInteraction.mscor >= mscor)
if correlation is not None:
if correlationDirection == "<":
queries_1.append(models.GeneInteraction.correlation <= correlation)
queries_2.append(models.GeneInteraction.correlation <= correlation)
else:
queries_1.append(models.GeneInteraction.correlation >= correlation)
queries_2.append(models.GeneInteraction.correlation >= correlation)
# add all sorting if given:
sort = []
if sorting is not None:
if sorting == "pValue":
if descending:
sort.append(models.GeneInteraction.p_value.desc())
else:
sort.append(models.GeneInteraction.p_value.asc())
if sorting == "mscor":
if descending:
sort.append(models.GeneInteraction.mscor.desc())
else:
sort.append(models.GeneInteraction.mscor.asc())
if sorting == "correlation":
if descending:
sort.append(models.GeneInteraction.correlation.desc())
else:
sort.append(models.GeneInteraction.correlation.asc())
# interaction_result = []
interaction_result = models.GeneInteraction.query \
.filter(*queries_1) \
.order_by(*sort) \
.union(models.GeneInteraction.query
.filter(*queries_2)
.order_by(*sort)) \
.slice(offset, offset + limit) \
.all()
# if len(tmp) > 0:
# interaction_result.append(tmp)
# else:
# abort(404, "No information with given parameters found")
# interaction_result = [val for sublist in interaction_result for val in sublist]
if len(interaction_result) > 0:
if information:
# Serialize the data for the response depending on parameter all
schema = models.GeneInteractionDatasetLongSchema(many=True)
else:
# Serialize the data for the response depending on parameter all
schema = models.GeneInteractionDatasetShortSchema(many=True)
return schema.dump(interaction_result).data
else:
abort(404, "No information with given parameters found")
def read_specific_interaction(disease_name=None, ensg_number=None, gene_symbol=None, pValue=0.05,
pValueDirection="<", limit=100, offset=0):
"""
This function responds to a request for /sponge/ceRNAInteraction/findSpecific
and returns all interactions between the given identifications (ensg_number or gene_symbol)
:param disease_name: disease_name of interest
:param ensg_number: esng number of the genes of interest
:param gene_symbol: gene symbol of the genes of interest
:param limit: number of results that shouls be shown
:param offset: startpoint from where results should be shown
:return: all interactions between given genes
"""
# test limit
if limit > 1000:
abort(404, "Limit is to high. For a high number of needed interactions please use the download section.")
# test if any of the two identification possibilites is given
if ensg_number is None and gene_symbol is None:
abort(404, "One of the two possible identification numbers must be provided")
if ensg_number is not None and gene_symbol is not None:
abort(404,
"More than one identifikation paramter is given. Please choose one out of (ensg number, gene symbol)")
gene = []
# if ensg_numer is given for specify gene, get the intern gene_ID(primary_key) for requested ensg_nr(gene_ID)
if ensg_number is not None:
gene = models.Gene.query \
.filter(models.Gene.ensg_number.in_(ensg_number)) \
.all()
elif gene_symbol is not None:
gene = models.Gene.query \
.filter(models.Gene.gene_symbol.in_(gene_symbol)) \
.all()
if len(gene) > 0:
gene_IDs = [i.gene_ID for i in gene]
else:
abort(404, "Not gene found for given ensg_number(s) or gene_symbol(s)")
# save all needed queries to get correct results
queries = [sa.and_(models.GeneInteraction.gene_ID1.in_(gene_IDs), models.GeneInteraction.gene_ID2.in_(gene_IDs))]
# if specific disease_name is given:
if disease_name is not None:
run = models.Run.query.join(models.Dataset, models.Dataset.dataset_ID == models.Run.dataset_ID) \
.filter(models.Dataset.disease_name.like("%" + disease_name + "%")) \
.all()
if len(run) > 0:
run_IDs = [i.run_ID for i in run]
queries.append(models.GeneInteraction.run_ID.in_(run_IDs))
else:
abort(404, "No dataset with given disease_name found")
# filter further depending on given statistics cutoffs
if pValue is not None:
if pValueDirection == "<":
queries.append(models.GeneInteraction.p_value < pValue)
else:
queries.append(models.GeneInteraction.p_value > pValue)
interaction_result = models.GeneInteraction.query \
.filter(*queries) \
.slice(offset, offset + limit) \
.all()
if len(interaction_result) > 0:
# Serialize the data for the response depending on parameter all
return models.GeneInteractionDatasetShortSchema(many=True).dump(interaction_result).data
else:
abort(404, "No information with given parameters found")
