class GoFastSemSimSimilarity(GoSimilarity):
# the biological process root node
#BP_root = "GO:0008150"
BP_root = 8150
def __init__(self, obo_file, assoc_file, sql_conn, verbose=True):
# load gene ontology with fastSemSim
if verbose:
print("loading gene ontology")
self.go = fastSemSim.Ontology.ontologies.load(obo_file)
# load annotation class with fastSemSim
if verbose:
print("creating annotation corpus")
self.ac = AnnotationCorpus(self.go)
# set parameters for accessions
ac_params = {}
# only keep go term and accession name in memory
ac_params['simplify'] = True
if verbose:
print("parsing association file")
self.ac.parse(assoc_file, "GOA")
# check for consistency
if not self.ac.isConsistent():
raise Exception
if verbose:
print("loading gene associations")
# TODO: might do this jointly with fastSemSim's associations
# this load all HGNC gene -> GO term associations into memory
self.assoc = self.load_go_associations(sql_conn)
if verbose:
print("creating BPscore object")
# load the semantic similarity class for SimRel (Schlicker et al. 2006)
# using the `max` function as mixer, resulting in an implementation
# for the BPScore metric from Schlicker et al. 2006
self.semsim_class = SetSemSim(self.go,
self.ac,
TSS="SimRel",
MSS="max")
#print(self.semsim_class.util.lineage)
def gene_pairwise_score(self, gene1, gene2):
# get similarity score for BP subtree
score = self.semsim_class.SemSim(self.assoc[gene1],
self.assoc[gene2],
root=self.BP_root)
# in case one gene is not annotated with a BP term, None is returned
# in that case the genes have 0 similarity
if score is None:
score = 0.0
return score
class GoFastSemSimSimilarity(GoSimilarity):
# the biological process root node
#BP_root = "GO:0008150"
BP_root = 8150
def __init__(self, obo_file, assoc_file, sql_conn, verbose=True):
# load gene ontology with fastSemSim
if verbose:
print("loading gene ontology")
self.go = fastSemSim.Ontology.ontologies.load(obo_file)
# load annotation class with fastSemSim
if verbose:
print("creating annotation corpus")
self.ac = AnnotationCorpus(self.go)
# set parameters for accessions
ac_params = {}
# only keep go term and accession name in memory
ac_params['simplify'] = True
if verbose:
print("parsing association file")
self.ac.parse(assoc_file, "GOA")
# check for consistency
if not self.ac.isConsistent():
raise Exception
if verbose:
print("loading gene associations")
# TODO: might do this jointly with fastSemSim's associations
# this load all HGNC gene -> GO term associations into memory
self.assoc = self.load_go_associations(sql_conn)
if verbose:
print("creating BPscore object")
# load the semantic similarity class for SimRel (Schlicker et al. 2006)
# using the `max` function as mixer, resulting in an implementation
# for the BPScore metric from Schlicker et al. 2006
self.semsim_class = SetSemSim(self.go, self.ac, TSS="SimRel",
MSS="max")
#print(self.semsim_class.util.lineage)
def gene_pairwise_score(self, gene1, gene2):
# get similarity score for BP subtree
score = self.semsim_class.SemSim(self.assoc[gene1],
self.assoc[gene2],
root=self.BP_root)
# in case one gene is not annotated with a BP term, None is returned
# in that case the genes have 0 similarity
if score is None:
score = 0.0
return score
def __init__(self, obo_file, assoc_file, sql_conn, verbose=True):
# load gene ontology with fastSemSim
if verbose:
print("loading gene ontology")
self.go = fastSemSim.Ontology.ontologies.load(obo_file)
# load annotation class with fastSemSim
if verbose:
print("creating annotation corpus")
self.ac = AnnotationCorpus(self.go)
# set parameters for accessions
ac_params = {}
# only keep go term and accession name in memory
ac_params['simplify'] = True
if verbose:
print("parsing association file")
self.ac.parse(assoc_file, "GOA")
# check for consistency
if not self.ac.isConsistent():
raise Exception
if verbose:
print("loading gene associations")
# TODO: might do this jointly with fastSemSim's associations
# this load all HGNC gene -> GO term associations into memory
self.assoc = self.load_go_associations(sql_conn)
if verbose:
print("creating BPscore object")
# load the semantic similarity class for SimRel (Schlicker et al. 2006)
# using the `max` function as mixer, resulting in an implementation
# for the BPScore metric from Schlicker et al. 2006
self.semsim_class = SetSemSim(self.go,
self.ac,
TSS="SimRel",
MSS="max")
def __init__(self, obo_file, assoc_file, sql_conn, verbose=True):
# load gene ontology with fastSemSim
if verbose:
print("loading gene ontology")
self.go = fastSemSim.Ontology.ontologies.load(obo_file)
# load annotation class with fastSemSim
if verbose:
print("creating annotation corpus")
self.ac = AnnotationCorpus(self.go)
# set parameters for accessions
ac_params = {}
# only keep go term and accession name in memory
ac_params['simplify'] = True
if verbose:
print("parsing association file")
self.ac.parse(assoc_file, "GOA")
# check for consistency
if not self.ac.isConsistent():
raise Exception
if verbose:
print("loading gene associations")
# TODO: might do this jointly with fastSemSim's associations
# this load all HGNC gene -> GO term associations into memory
self.assoc = self.load_go_associations(sql_conn)
if verbose:
print("creating BPscore object")
# load the semantic similarity class for SimRel (Schlicker et al. 2006)
# using the `max` function as mixer, resulting in an implementation
# for the BPScore metric from Schlicker et al. 2006
self.semsim_class = SetSemSim(self.go, self.ac, TSS="SimRel",
MSS="max")
