def go_enrch_net(meth, net_obj_id=None, p_value=0.05, ec=None, domain=None):
"""Identify Gene Ontology terms enriched in individual network clusters
:param net_obj_id: Cluster object id
:type net_obj_id: kbtypes.KBaseNetworks.Network
:param p_value: p-value cutoff
:type p_value: kbtypes.Unicode
:param ec: Evidence code list (comma separated, IEA, ...)
:type ec:kbtypes.Unicode
:param domain: Domain list (comma separated, biological_process, ...)
:type domain: kbtypes.Unicode
:return: Workspace id
:rtype: kbtypes.Unicode
:output_widget: GeneTableWidget
"""
# :default p_value: 0.05
meth.stages = 3
meth.advance("Prepare enrichment test")
gc = GWAS(URLS.gwas, token=meth.token)
ec = ec.replace(" ", "")
domain = domain.replace(" ", "")
ec_list = [i for i in ec.split(',')]
domain_list = [i for i in domain.split(',')]
wsd = Workspace2(token=meth.token, wsid=meth.workspace_id)
oc = Ontology(url=URLS.ontology)
net_object = wsd.get_objects([{
'workspace': meth.workspace_id,
'name': net_obj_id
}])
nc = Node(net_object[0]['data']['nodes'], net_object[0]['data']['edges'])
gids = [
i for i in sorted(nc.ugids.keys()) if 'CDS' in i or 'locus' in i or (
not 'clst' in i and not i.startswith('cluster'))
]
gids2cds = ids2cds(gids)
meth.advance("Run enrichment test for each clusters")
rows = []
for hr_nd in net_object[0]['data']['nodes']:
gid = hr_nd['entity_id']
if not (gid.startswith('cluster.') or 'clst' in gid): continue
glist = nc.get_gene_list(gid)
# now everything is in CDS id
#llist = []
#for i in glist:
# if i in mrnas: i = mrnas[i]
# if i in locus: i = locus[i]
# if 'locus' in i: llist.append(i)
#llist = [ locus[mrnas[i]] ]; # it will ignore orignal locus ids (TODO: keep locus)
cds_gl = [gids2cds[i] for i in glist]
enr_list = oc.get_go_enrichment(cds_gl, domain_list, ec_list,
'hypergeometric', 'GO')
enr_list = sorted(enr_list, key=itemgetter('pvalue'), reverse=False)
go_enr_smry = ""
go_enr_anns = ["", "", ""]
for i in range(len(enr_list)):
goen = enr_list[i]
if goen['pvalue'] > float(p_value): continue
hr_nd['user_annotations']['gce.' + goen['goID'] +
".desc"] = goen['goDesc'][0]
hr_nd['user_annotations']['gce.' + goen['goID'] +
".domain"] = goen['goDesc'][1]
hr_nd['user_annotations']['gce.' + goen['goID'] +
".p_value"] = ` goen['pvalue'] `
if i < 3:
go_enr_smry += goen['goID'] + "(" + "{:6.4f}".format(
goen['pvalue']) + ")" + goen['goDesc'][0] + "\n"
go_enr_anns[i] = goen['goID'] + "(" + "{:6.4f}".format(
goen['pvalue']) + ")" + goen['goDesc'][0]
hr_nd['user_annotations']['go_enrichnment_annotation'] = go_enr_smry
rows.append(
[gid,
len(glist), go_enr_anns[0], go_enr_anns[1], go_enr_anns[2]])
wsd.save_objects({
'workspace':
meth.workspace_id,
'objects': [{
'type': 'KBaseNetworks.Network',
'data': net_object[0]['data'],
'name': net_obj_id + ".cenr",
'meta': {
'orginal': net_obj_id
}
}]
})
rows = sorted(rows, key=lambda x: x[1], reverse=True)
#meth.debug("rows: {}".format(rows))
header = [
"Cluster ID", "# of Genes", "Annotation1", "Annotation2", "Annotation3"
]
data = {'table': [header] + rows}
return json.dumps(data)
def featureset_net_enr(meth,
feature_set_id=None,
p_value=None,
ref_wsid="KBasePublicNetwork",
ref_network=None,
out_id=None):
"""This method annotate GO terms and execute GO enrichment test
:param feature_set_id: FeatureSet workspace object id
:type feature_set_id: kbtypes.KBaseSearch.FeatureSet
:param p_value: p-value cutoff
:type p_value: kbtypes.Unicode
:param ref_wsid: Reference Network workspace id (optional, default to current workspace)
:type ref_wsid: kbtypes.Unicode
:param ref_network: Reference Network object name
:type ref_network:kbtypes.KBaseNetworks.Network
:param out_id: Output FeatureSet object identifier
:type out_id: kbtypes.KBaseSearch.FeatureSet
:return: New workspace object
:rtype: kbtypes.Unicode
:output_widget: GeneTableWidget
"""
meth.stages = 3
meth.advance("Prepare Enrichment Test")
oc = Ontology(url=URLS.ontology)
ws = Workspace2(token=meth.token, wsid=meth.workspace_id)
fs = ws.get(feature_set_id)
if not ref_wsid: ref_wsid = meth.workspace_id
ws2 = Workspace2(token=meth.token, wsid=ref_wsid)
net = ws2.get(ref_network)
# checking user input
if 'edges' not in net or 'nodes' not in net or 'elements' not in fs:
return "{}"
qid2cds = ids2cds(fs['elements'].keys())
# parse networks object
nc = Node(net['nodes'], net['edges'])
meth.advance("Execute Enrichment Test")
qcdss = set(qid2cds.values())
enr_dict = oc.association_test(list(qcdss), ref_wsid, ref_network, '',
'hypergeometric', 'none', p_value)
enr_list = sorted([(value, key) for (key, value) in enr_dict.items()])
nid2name = {}
for ne in net['nodes']:
nid2name[ne['entity_id']] = ne['name']
pwy_enr_smry = ""
header = ["Pathway ID", "Name", "p-value", "FeatureSet ID (# genes)"]
fields = []
objects = []
for i in range(len(enr_list)):
pwy_en = enr_list[i]
if float(pwy_en[0]) > float(p_value): continue
cgenes = set(nc.get_gene_list(pwy_en[1]))
cgenes = list(cgenes.intersection(qcdss))
cfs = genelist2fs(cgenes)
fields.append([
pwy_en[1], nid2name[pwy_en[1]],
"{:12.10f}".format(float(pwy_en[0])),
out_id + "_to_" + pwy_en[1] + "({})".format(len(cgenes))
])
objects.append({
'type': 'KBaseSearch.FeatureSet',
'data': cfs,
'name': out_id + "_to_" + pwy_en[1],
'meta': {
'original': feature_set_id,
'ref_wsid': ref_wsid,
'ref_net': ref_network,
'pwy_id': pwy_en[1]
}
})
if i < 3:
pwy_enr_smry += pwy_en[1] + "(" + "{:6.4f}".format(float(
pwy_en[0])) + ")" + nid2name[pwy_en[1]] + "\n"
data = {'table': [header] + fields}
meth.advance("Saving output to Workspace")
objects.append({
'type': 'KBaseSearch.FeatureSet',
'data': fs,
'name': out_id,
'meta': {
'original': feature_set_id,
'ref_wsid': ref_wsid,
'ref_net': ref_network,
'pwy_enr_summary': pwy_enr_smry
}
})
ws.save_objects({'workspace': meth.workspace_id, 'objects': objects})
meth.advance("Returning object")
return json.dumps(data)
def go_anno_net(meth, net_obj_id=None):
"""Add Gene Ontology annotation to network gene nodes
:param net_obj_id: Network object id
:type net_obj_id: kbtypes.KBaseNetworks.Network
:return: Workspace id
:rtype: kbtypes.Unicode
:output_widget: ValueListWidget
"""
meth.stages = 5
meth.advance("Prepare annotation service")
#gc = GWAS(URLS.gwas, token=meth.token)
# load from current or other workspace
wsid = meth.workspace_id
# save to current workspace
ws_save_id = meth.workspace_id
meth.advance("Load network object")
wsd = Workspace2(token=meth.token, wsid=wsid)
oc = Ontology(url=URLS.ontology)
net_object = wsd.get(net_obj_id)
nc = Node(net_object['nodes'], net_object['edges'])
idc = IDServerAPI(URLS.ids)
cdmic = CDMI_API(URLS.cdmi)
cdmie = CDMI_EntityAPI(URLS.cdmi)
#idm = IdMap(URLS.idmap)
gids = [
i for i in sorted(nc.ugids.keys()) if 'CDS' in i or 'locus' in i or (
not 'clst' in i and not i.startswith('cluster') and 'ps.' not in i)
]
meth.advance("Get relationships from central data model")
#eids = idc.kbase_ids_to_external_ids(gids)
eids = kb_id2ext_id(idc, gids, 100)
gids2cds = ids2cds(gids)
cgids = gids2cds.values()
cds2l = cds2locus(cgids)
#mrnas_l = cdmie.get_relationship_Encompasses(gids, [], ['to_link'], [])
#mrnas = dict((i[1]['from_link'], i[1]['to_link']) for i in mrnas_l)
#locus_l = cdmie.get_relationship_Encompasses(mrnas.values(), [], ['to_link'], [])
#locus = dict((i[1]['from_link'], i[1]['to_link']) for i in locus_l)
#lgids = [locus[mrnas[i]] for i in gids if i in mrnas.keys()] # ignore original locus ids in gids
lgids = cds2l.values()
meth.advance("Annotate ({:d} nodes, {:d} edges)".format(
len(net_object['nodes']), len(net_object['edges'])))
#ots = oc.get_goidlist(lgids, ['biological_process'], ['IEA'])
ots = oc.get_goidlist(cgids, [], [])
oan = () #oc.get_go_annotation(lgids)
funcs = cdmic.fids_to_functions(lgids)
funcs_org = cdmic.fids_to_functions(cgids)
annotate_nodes(net_object,
ots=ots,
oan=oan,
funcs=funcs,
funcs_org=funcs_org,
eids=eids,
gids2cds=gids2cds,
cds2l=cds2l)
meth.advance("Save annotated object to workspace {}".format(ws_save_id))
obj = {
'type': 'KBaseNetworks.Network',
'data': net_object,
'name': net_obj_id + ".ano",
'meta': {
'original': net_obj_id
}
}
wsd.save_objects({'workspace': ws_save_id, 'objects': [obj]})
return _workspace_output(net_obj_id + ".ano")
def featureset_go_anal(meth,
feature_set_id=None,
p_value=0.05,
ec='IEA',
domain='biological_process',
out_id=None):
"""This method annotate GO terms and execute GO enrichment test
:param feature_set_id: FeatureSet workspace object id
:type feature_set_id: kbtypes.KBaseSearch.FeatureSet
:param p_value: p-value cutoff
:type p_value: kbtypes.Unicode
:param ec: Evidence code list (comma separated, IEA,ISS,IDA,IEP,IPI,RCA ..)
:type ec:kbtypes.Unicode
:param domain: Domain list (comma separated, biological_process,molecular_function,cellular_component)
:type domain: kbtypes.Unicode
:param out_id: Output FeatureSet object identifier
:type out_id: kbtypes.KBaseSearch.FeatureSet
:return: New workspace object
:rtype: kbtypes.Unicode
:output_widget: GeneTableWidget
"""
meth.stages = 4
meth.advance("Prepare Enrichment Test")
oc = Ontology(url=URLS.ontology)
ws = Workspace2(token=meth.token, wsid=meth.workspace_id)
fs = ws.get(feature_set_id)
qid2cds = ids2cds(fs['elements'].keys())
cds2l = cds2locus(qid2cds.values())
cdmic = CDMI_API(URLS.cdmi)
lfunc = cdmic.fids_to_functions(cds2l.values())
meth.advance("Annotate GO Term")
ec = ec.replace(" ", "")
domain = domain.replace(" ", "")
ec_list = [i for i in ec.split(',')]
domain_list = [i for i in domain.split(',')]
ots = oc.get_goidlist(list(set(qid2cds.values())), domain_list, ec_list)
go_key = lambda go, i, ext: "go.{}.{:d}.{}".format(go, i, ext)
go2cds = {}
for gid in fs['elements']:
lid = qid2cds[gid]
if 'data' in fs['elements'][gid]:
if not fs['elements'][gid]['data']['function']:
fs['elements'][gid]['data']['function'] = lfunc[cds2l[lid]]
if 'metadata' not in fs['elements'][gid]:
fs['elements'][gid]['metadata'] = {}
if lid in ots:
go_enr_list = []
for lcnt, go in enumerate(ots[lid].keys()):
if go not in go2cds: go2cds[go] = set()
go2cds[go].add(lid)
for i, goen in enumerate(ots[lid][go]):
for ext in "domain", "ec", "desc":
fs['elements'][gid]['metadata'][go_key(
go, i, ext)] = goen[ext]
fs['elements'][gid]['metadata'][go_key(
go, i, ext)] = goen[ext]
meth.advance("Execute Enrichment Test")
enr_list = oc.get_go_enrichment(list(set(qid2cds.values())), domain_list,
ec_list, 'hypergeometric', 'GO')
enr_list = sorted(enr_list, key=itemgetter('pvalue'), reverse=False)
header = [
"GO ID", "Description", "Domain", "p-value", "FeatureSet ID (# genes)"
]
fields = []
objects = []
go_enr_smry = ""
for i in range(len(enr_list)):
goen = enr_list[i]
if goen['pvalue'] > float(p_value): continue
cfs = genelist2fs(list(go2cds[goen['goID']]))
goid = goen['goID'].replace(":", "")
fields.append([
goen['goID'], goen['goDesc'][0], goen['goDesc'][1],
"{:12.10f}".format(goen['pvalue']),
"{}_to_{} ({})".format(out_id, goid, len(go2cds[goen['goID']]))
])
objects.append({
'type': 'KBaseSearch.FeatureSet',
'data': cfs,
'name': out_id + "_to_" + goid,
'meta': {
'original': feature_set_id,
'domain': domain,
'ec': ec,
'GO_ID': goen['goID']
}
})
if i < 3:
go_enr_smry += goen['goID'] + "(" + "{:6.4f}".format(
goen['pvalue']) + ")" + goen['goDesc'][0] + "\n"
go_enr_smry
data = {'table': [header] + fields}
meth.advance("Saving output to Workspace")
objects.append({
'type': 'KBaseSearch.FeatureSet',
'data': fs,
'name': out_id,
'meta': {
'original': feature_set_id,
'enr_summary': go_enr_smry
}
})
ws.save_objects({'workspace': meth.workspace_id, 'objects': objects})
return json.dumps(data)
def go_enrch_net(meth, net_obj_id=None, p_value=0.05, ec=None, domain=None):
"""Identify Gene Ontology terms enriched in individual network clusters
:param net_obj_id: Cluster object id
:type net_obj_id: kbtypes.KBaseNetworks.Network
:param p_value: p-value cutoff
:type p_value: kbtypes.Unicode
:param ec: Evidence code list (comma separated, IEA, ...)
:type ec:kbtypes.Unicode
:param domain: Domain list (comma separated, biological_process, ...)
:type domain: kbtypes.Unicode
:return: Workspace id
:rtype: kbtypes.Unicode
:output_widget: GeneTableWidget
"""
# :default p_value: 0.05
meth.stages = 3
meth.advance("Prepare enrichment test")
gc = GWAS(URLS.gwas, token=meth.token)
ec = ec.replace(" ","")
domain = domain.replace(" ","")
ec_list = [ i for i in ec.split(',')]
domain_list = [ i for i in domain.split(',')]
wsd = Workspace2(token=meth.token, wsid=meth.workspace_id)
oc = Ontology(url=URLS.ontology)
net_object = wsd.get_objects([{'workspace' : meth.workspace_id, 'name' : net_obj_id}]);
nc = Node(net_object[0]['data']['nodes'], net_object[0]['data']['edges'])
gids = [ i for i in sorted(nc.ugids.keys()) if 'CDS' in i or 'locus' in i or (not 'clst' in i and not i.startswith('cluster'))]
gids2cds = ids2cds(gids)
meth.advance("Run enrichment test for each clusters")
rows = []
for hr_nd in net_object[0]['data']['nodes']:
gid = hr_nd['entity_id']
if not (gid.startswith('cluster.') or 'clst' in gid ): continue
glist = nc.get_gene_list(gid)
# now everything is in CDS id
#llist = []
#for i in glist:
# if i in mrnas: i = mrnas[i]
# if i in locus: i = locus[i]
# if 'locus' in i: llist.append(i)
#llist = [ locus[mrnas[i]] ]; # it will ignore orignal locus ids (TODO: keep locus)
cds_gl = [gids2cds[i] for i in glist]
enr_list = oc.get_go_enrichment(cds_gl, domain_list, ec_list, 'hypergeometric', 'GO')
enr_list = sorted(enr_list, key=itemgetter('pvalue'), reverse=False)
go_enr_smry = "";
go_enr_anns = ["", "", ""]
for i in range(len(enr_list)):
goen = enr_list[i]
if goen['pvalue'] > float(p_value) : continue
hr_nd['user_annotations']['gce.'+goen['goID']+".desc" ] = goen['goDesc'][0]
hr_nd['user_annotations']['gce.'+goen['goID']+".domain" ] = goen['goDesc'][1]
hr_nd['user_annotations']['gce.'+goen['goID']+".p_value" ] = `goen['pvalue']`
if i < 3 :
go_enr_smry += goen['goID']+"(" + "{:6.4f}".format(goen['pvalue']) + ")" + goen['goDesc'][0] + "\n"
go_enr_anns[i] = goen['goID']+"(" + "{:6.4f}".format(goen['pvalue']) + ")" + goen['goDesc'][0]
hr_nd['user_annotations']['go_enrichnment_annotation'] = go_enr_smry
rows.append([gid,len(glist),go_enr_anns[0],go_enr_anns[1],go_enr_anns[2]])
wsd.save_objects({'workspace' : meth.workspace_id, 'objects' : [{'type' : 'KBaseNetworks.Network', 'data' : net_object[0]['data'], 'name' : net_obj_id + ".cenr", 'meta' : {'orginal' : net_obj_id}}]})
rows = sorted(rows, key=lambda x: x[1], reverse=True)
#meth.debug("rows: {}".format(rows))
header = ["Cluster ID", "# of Genes", "Annotation1", "Annotation2", "Annotation3"]
data = {'table': [header] + rows}
return json.dumps(data)
def go_anno_net(meth, net_obj_id=None):
"""Add Gene Ontology annotation to network gene nodes
:param net_obj_id: Network object id
:type net_obj_id: kbtypes.KBaseNetworks.Network
:return: Workspace id
:rtype: kbtypes.Unicode
:output_widget: ValueListWidget
"""
meth.stages = 5
meth.advance("Prepare annotation service")
#gc = GWAS(URLS.gwas, token=meth.token)
# load from current or other workspace
wsid = meth.workspace_id
# save to current workspace
ws_save_id = meth.workspace_id
meth.advance("Load network object")
wsd = Workspace2(token=meth.token, wsid=wsid)
oc = Ontology(url=URLS.ontology)
net_object = wsd.get(net_obj_id)
nc = Node(net_object['nodes'], net_object['edges'])
idc = IDServerAPI(URLS.ids)
cdmic = CDMI_API(URLS.cdmi)
cdmie = CDMI_EntityAPI(URLS.cdmi)
#idm = IdMap(URLS.idmap)
gids = [i for i in sorted(nc.ugids.keys())
if 'CDS' in i or 'locus' in i or (not 'clst' in i and not i.startswith('cluster') and 'ps.' not in i )]
meth.advance("Get relationships from central data model")
#eids = idc.kbase_ids_to_external_ids(gids)
eids = kb_id2ext_id(idc, gids, 100)
gids2cds = ids2cds(gids)
cgids = gids2cds.values()
cds2l = cds2locus(cgids)
#mrnas_l = cdmie.get_relationship_Encompasses(gids, [], ['to_link'], [])
#mrnas = dict((i[1]['from_link'], i[1]['to_link']) for i in mrnas_l)
#locus_l = cdmie.get_relationship_Encompasses(mrnas.values(), [], ['to_link'], [])
#locus = dict((i[1]['from_link'], i[1]['to_link']) for i in locus_l)
#lgids = [locus[mrnas[i]] for i in gids if i in mrnas.keys()] # ignore original locus ids in gids
lgids = cds2l.values()
meth.advance("Annotate ({:d} nodes, {:d} edges)".format(
len(net_object['nodes']), len(net_object['edges'])))
#ots = oc.get_goidlist(lgids, ['biological_process'], ['IEA'])
ots = oc.get_goidlist(cgids, [], [])
oan = () #oc.get_go_annotation(lgids)
funcs = cdmic.fids_to_functions(lgids)
funcs_org = cdmic.fids_to_functions(cgids)
annotate_nodes(net_object, ots=ots, oan=oan, funcs=funcs, funcs_org=funcs_org, eids=eids,
gids2cds=gids2cds, cds2l=cds2l)
meth.advance("Save annotated object to workspace {}".format(ws_save_id))
obj = {
'type': 'KBaseNetworks.Network',
'data': net_object,
'name': net_obj_id + ".ano",
'meta': {
'original': net_obj_id
}
}
wsd.save_objects({'workspace': ws_save_id, 'objects': [obj]})
return _workspace_output(net_obj_id + ".ano")
def featureset_net_enr(meth, feature_set_id=None, p_value=None, ref_wsid="KBasePublicNetwork", ref_network=None, out_id=None):
"""This method annotate GO terms and execute GO enrichment test
:param feature_set_id: FeatureSet workspace object id
:type feature_set_id: kbtypes.KBaseSearch.FeatureSet
:param p_value: p-value cutoff
:type p_value: kbtypes.Unicode
:param ref_wsid: Reference Network workspace id (optional, default to current workspace)
:type ref_wsid: kbtypes.Unicode
:param ref_network: Reference Network object name
:type ref_network:kbtypes.KBaseNetworks.Network
:param out_id: Output FeatureSet object identifier
:type out_id: kbtypes.KBaseSearch.FeatureSet
:return: New workspace object
:rtype: kbtypes.Unicode
:output_widget: GeneTableWidget
"""
meth.stages = 3
meth.advance("Prepare Enrichment Test")
oc = Ontology(url=URLS.ontology)
ws = Workspace2(token=meth.token, wsid=meth.workspace_id)
fs = ws.get(feature_set_id)
if not ref_wsid : ref_wsid = meth.workspace_id
ws2 = Workspace2(token=meth.token, wsid=ref_wsid)
net = ws2.get(ref_network)
# checking user input
if 'edges' not in net or 'nodes' not in net or 'elements' not in fs: return "{}"
qid2cds = ids2cds(fs['elements'].keys())
# parse networks object
nc = Node(net['nodes'],net['edges']);
meth.advance("Execute Enrichment Test")
qcdss = set(qid2cds.values())
enr_dict = oc.association_test(list(qcdss), ref_wsid, ref_network, '', 'hypergeometric', 'none', p_value)
enr_list = sorted([(value,key) for (key,value) in enr_dict.items()])
nid2name = {}
for ne in net['nodes']:
nid2name[ne['entity_id']] = ne['name']
pwy_enr_smry = ""
header = ["Pathway ID", "Name", "p-value", "FeatureSet ID (# genes)"]
fields = []
objects = []
for i in range(len(enr_list)):
pwy_en = enr_list[i]
if float(pwy_en[0]) > float(p_value) : continue
cgenes = set(nc.get_gene_list(pwy_en[1]))
cgenes = list(cgenes.intersection(qcdss))
cfs = genelist2fs(cgenes)
fields.append([pwy_en[1], nid2name[pwy_en[1]], "{:12.10f}".format(float(pwy_en[0])), out_id + "_to_" + pwy_en[1] + "({})".format(len(cgenes))])
objects.append({'type' : 'KBaseSearch.FeatureSet', 'data' : cfs, 'name' : out_id + "_to_" + pwy_en[1], 'meta' : {'original' : feature_set_id, 'ref_wsid' : ref_wsid, 'ref_net' : ref_network, 'pwy_id' :pwy_en[1]}})
if i < 3 :
pwy_enr_smry += pwy_en[1]+"(" + "{:6.4f}".format(float(pwy_en[0])) + ")" + nid2name[pwy_en[1]] + "\n"
data = {'table': [header] + fields}
meth.advance("Saving output to Workspace")
objects.append({'type' : 'KBaseSearch.FeatureSet', 'data' : fs, 'name' : out_id, 'meta' : {'original' : feature_set_id, 'ref_wsid' : ref_wsid, 'ref_net' : ref_network, 'pwy_enr_summary' :pwy_enr_smry}})
ws.save_objects({'workspace' : meth.workspace_id, 'objects' :objects})
meth.advance("Returning object")
return json.dumps(data)
def featureset_go_anal(meth, feature_set_id=None, p_value=0.05, ec='IEA', domain='biological_process', out_id=None):
"""This method annotate GO terms and execute GO enrichment test
:param feature_set_id: FeatureSet workspace object id
:type feature_set_id: kbtypes.KBaseSearch.FeatureSet
:param p_value: p-value cutoff
:type p_value: kbtypes.Unicode
:param ec: Evidence code list (comma separated, IEA,ISS,IDA,IEP,IPI,RCA ..)
:type ec:kbtypes.Unicode
:param domain: Domain list (comma separated, biological_process,molecular_function,cellular_component)
:type domain: kbtypes.Unicode
:param out_id: Output FeatureSet object identifier
:type out_id: kbtypes.KBaseSearch.FeatureSet
:return: New workspace object
:rtype: kbtypes.Unicode
:output_widget: GeneTableWidget
"""
meth.stages = 4
meth.advance("Prepare Enrichment Test")
oc = Ontology(url=URLS.ontology)
ws = Workspace2(token=meth.token, wsid=meth.workspace_id)
fs = ws.get(feature_set_id)
qid2cds = ids2cds(fs['elements'].keys())
cds2l = cds2locus(qid2cds.values())
cdmic = CDMI_API(URLS.cdmi)
lfunc = cdmic.fids_to_functions(cds2l.values())
meth.advance("Annotate GO Term")
ec = ec.replace(" ","")
domain = domain.replace(" ","")
ec_list = [ i for i in ec.split(',')]
domain_list = [ i for i in domain.split(',')]
ots = oc.get_goidlist(list(set(qid2cds.values())), domain_list, ec_list)
go_key = lambda go, i, ext: "go.{}.{:d}.{}".format(go, i, ext)
go2cds = {}
for gid in fs['elements']:
lid = qid2cds[gid]
if 'data' in fs['elements'][gid]:
if not fs['elements'][gid]['data']['function']: fs['elements'][gid]['data']['function'] = lfunc[cds2l[lid]]
if 'metadata' not in fs['elements'][gid]: fs['elements'][gid]['metadata'] = {}
if lid in ots:
go_enr_list = []
for lcnt, go in enumerate(ots[lid].keys()):
if go not in go2cds: go2cds[go] = set()
go2cds[go].add(lid)
for i, goen in enumerate(ots[lid][go]):
for ext in "domain", "ec", "desc":
fs['elements'][gid]['metadata'][go_key(go, i, ext)] = goen[ext]
fs['elements'][gid]['metadata'][go_key(go, i, ext)] = goen[ext]
meth.advance("Execute Enrichment Test")
enr_list = oc.get_go_enrichment(list(set(qid2cds.values())), domain_list, ec_list, 'hypergeometric', 'GO')
enr_list = sorted(enr_list, key=itemgetter('pvalue'), reverse=False)
header = ["GO ID", "Description", "Domain", "p-value", "FeatureSet ID (# genes)"]
fields = []
objects = []
go_enr_smry = ""
for i in range(len(enr_list)):
goen = enr_list[i]
if goen['pvalue'] > float(p_value) : continue
cfs = genelist2fs(list(go2cds[goen['goID']]))
goid = goen['goID'].replace(":","")
fields.append([goen['goID'], goen['goDesc'][0], goen['goDesc'][1], "{:12.10f}".format(goen['pvalue']), "{}_to_{} ({})".format(out_id, goid,len(go2cds[goen['goID']])) ])
objects.append({'type' : 'KBaseSearch.FeatureSet', 'data' : cfs, 'name' : out_id + "_to_" + goid, 'meta' : {'original' : feature_set_id, 'domain' : domain, 'ec' : ec, 'GO_ID' :goen['goID']}})
if i < 3 :
go_enr_smry += goen['goID']+"(" + "{:6.4f}".format(goen['pvalue']) + ")" + goen['goDesc'][0] + "\n"
go_enr_smry
data = {'table': [header] + fields}
meth.advance("Saving output to Workspace")
objects.append({'type' : 'KBaseSearch.FeatureSet', 'data' : fs, 'name' : out_id, 'meta' : {'original' : feature_set_id, 'enr_summary' : go_enr_smry}})
ws.save_objects({'workspace' : meth.workspace_id, 'objects' :objects})
return json.dumps(data)
