def gen_gene_id_list(trait_list):
trait_name_list = []
gene_id_list = []
for trait_db in trait_list:
trait = trait_db[0]
trait_name_list.append(trait.name)
retrieve_trait_info(trait, trait.dataset)
gene_id_list.append(str(trait.geneid))
return trait_name_list, gene_id_list
def view_collection():
params = request.args
uc_id = params['uc_id']
uc = (collection for collection in g.user_session.user_collections
if collection["id"] == uc_id).next()
traits = uc["members"]
trait_obs = []
json_version = []
for atrait in traits:
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(dataset_name,
dataset_type="Temp",
group_name=group)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
trait_ob = trait.retrieve_trait_info(trait_ob,
dataset,
get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(trait.jsonable(trait_ob))
collection_info = dict(trait_obs=trait_obs, uc=uc)
if "json" in params:
return json.dumps(json_version)
else:
return render_template("collections/view.html", **collection_info)
def test_retrieve_trait_info_with_empty_lrs_field(self, resource_id_mock,
g_mock, requests_mock):
"""Test retrieve trait info with empty lrs field"""
resource_id_mock.return_value = 1
g_mock.db.execute.return_value.fetchone = mock.Mock()
g_mock.db.execute.return_value.fetchone.side_effect = [
[1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone()
[1, None, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone()
[2, 3] # trait_info = g.db.execute(query).fetchone()
]
requests_mock.return_value = None
mock_dataset = mock.MagicMock()
type(mock_dataset).display_fields = mock.PropertyMock(
return_value=["a", "b", "c", "d"])
type(mock_dataset).type = "ProbeSet"
type(mock_dataset).name = "RandomName"
mock_trait = MockTrait(dataset=mock_dataset,
pre_publication_description="test_string")
trait_attrs = {
"description": "some description",
"probe_target_description": "some description",
"cellid": False,
"chr": 2.733,
"mb": 2.1204
}
for key, val in list(trait_attrs.items()):
setattr(mock_trait, key, val)
test_trait = retrieve_trait_info(trait=mock_trait,
dataset=mock_dataset,
get_qtl_info=True)
self.assertEqual(test_trait.LRS_score_repr, "N/A")
self.assertEqual(test_trait.LRS_location_repr, "Chr2: 3.000000")
def test_retrieve_trait_info_utf8_parsing(self, requests_mock):
"""Test that utf-8 strings are parsed correctly"""
utf_8_string = "test_string"
mock_dataset = mock.MagicMock()
requests_mock.return_value = TestResponse()
type(mock_dataset).display_fields = mock.PropertyMock(
return_value=["a", "b", "c", "d"])
type(mock_dataset).type = 'Publish'
mock_trait = MockTrait(dataset=mock_dataset,
pre_publication_description=utf_8_string)
trait_attrs = {
"group_code": "test_code",
"pre_publication_description": "test_pre_pub",
"pre_publication_abbreviation": "ファイルを画面毎に見て行くには、次のコマンドを使います。",
"post_publication_description": None,
"pubmed_id": None,
'year': "2020",
"authors": "Jane Doe かいと",
}
for key, val in list(trait_attrs.items()):
setattr(mock_trait, key, val)
test_trait = retrieve_trait_info(trait=mock_trait,
dataset=mock_dataset)
self.assertEqual(test_trait.abbreviation,
"ファイルを画面毎に見て行くには、次のコマンドを使います。")
self.assertEqual(test_trait.authors, "Jane Doe かいと")
def test_retrieve_trait_info_with_non_empty_trait_info(
self, requests_mock):
"""Test that attributes are set"""
mock_dataset = mock.MagicMock()
requests_mock.return_value = TestResponse()
type(mock_dataset).display_fields = mock.PropertyMock(
return_value=["a", "b", "c", "d"])
test_trait = retrieve_trait_info(trait=MockTrait(dataset=mock_dataset),
dataset=mock_dataset)
self.assertEqual(test_trait.a, 1)
self.assertEqual(test_trait.b, 2)
self.assertEqual(test_trait.c, 3)
self.assertEqual(test_trait.d, 4)
def view_collection():
params = request.args
print("PARAMS in view collection:", params)
if "uc_id" in params:
uc_id = params['uc_id']
uc = model.UserCollection.query.get(uc_id)
traits = json.loads(uc.members)
else:
user_collections = json.loads(Redis.get(user_manager.AnonUser().key))
this_collection = {}
for collection in user_collections:
if collection['id'] == params['collection_id']:
this_collection = collection
break
#this_collection = user_collections[params['collection_id']]
traits = this_collection['members']
print("in view_collection traits are:", traits)
trait_obs = []
json_version = []
for atrait in traits:
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(dataset_name, dataset_type = "Temp", group_name = group)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
trait_ob = trait.retrieve_trait_info(trait_ob, dataset, get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(trait.jsonable(trait_ob))
if "uc_id" in params:
collection_info = dict(trait_obs=trait_obs,
uc = uc)
else:
collection_info = dict(trait_obs=trait_obs,
collection_name=this_collection['name'])
if "json" in params:
print("json_version:", json_version)
return json.dumps(json_version)
else:
return render_template("collections/view.html",
**collection_info
)
def view_collection():
params = request.args
if g.user_session.logged_in and "uc_id" in params:
uc_id = params['uc_id']
uc = (collection for collection in g.user_session.user_collections if collection["id"] == uc_id).next()
traits = uc["members"]
else:
user_collections = json.loads(Redis.get(user_manager.AnonUser().key))
this_collection = {}
for collection in user_collections:
if collection['id'] == params['collection_id']:
this_collection = collection
break
traits = this_collection['members']
trait_obs = []
json_version = []
for atrait in traits:
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(dataset_name, dataset_type = "Temp", group_name = group)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
trait_ob = trait.retrieve_trait_info(trait_ob, dataset, get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(trait.jsonable(trait_ob))
if "uc_id" in params:
collection_info = dict(trait_obs=trait_obs,
uc = uc)
else:
collection_info = dict(trait_obs=trait_obs,
collection_name=this_collection['name'])
if "json" in params:
return json.dumps(json_version)
else:
return render_template("collections/view.html",
**collection_info
)
def view_collection():
params = request.args
uc_id = params['uc_id']
uc = next(
(collection for collection in g.user_session.user_collections if collection["id"] == uc_id))
traits = uc["members"]
trait_obs = []
json_version = []
for atrait in traits:
if ':' not in atrait:
continue
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(
dataset_name, dataset_type="Temp", group_name=group)
trait_ob = create_trait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = create_trait(name=name, dataset=dataset)
trait_ob = retrieve_trait_info(
trait_ob, dataset, get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(jsonable(trait_ob))
collection_info = dict(
trait_obs=trait_obs,
uc=uc,
heatmap_data_url=f"{GN_SERVER_URL}heatmaps/clustered")
if "json" in params:
return json.dumps(json_version)
else:
return render_template("collections/view.html",
**collection_info
)
def export_search_results_csv(targs):
table_data = json.loads(targs['export_data'])
table_rows = table_data['rows']
now = datetime.datetime.now()
time_str = now.strftime('%H:%M_%d%B%Y')
if 'file_name' in targs:
zip_file_name = targs['file_name'] + "_export_" + time_str
else:
zip_file_name = "export_" + time_str
metadata = []
if 'database_name' in targs:
if targs['database_name'] != "None":
metadata.append(["Data Set: " + targs['database_name']])
if 'accession_id' in targs:
if targs['accession_id'] != "None":
metadata.append([
"Metadata Link: http://genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId="
+ targs['accession_id']
])
metadata.append(
["Export Date: " + datetime.datetime.now().strftime("%B %d, %Y")])
metadata.append(
["Export Time: " + datetime.datetime.now().strftime("%H:%M GMT")])
if 'search_string' in targs:
if targs['search_string'] != "None":
metadata.append(["Search Query: " + targs['search_string']])
if 'filter_term' in targs:
if targs['filter_term'] != "None":
metadata.append(["Search Filter Terms: " + targs['filter_term']])
metadata.append(["Exported Row Number: " + str(len(table_rows))])
metadata.append([
"Funding for The GeneNetwork: NIGMS (R01 GM123489, 2017-2021), NIDA (P30 DA044223, 2017-2022), NIA (R01AG043930, 2013-2018), NIAAA (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017), NIDA/NIMH/NIAAA (P20-DA 21131, 2001-2012), NCI MMHCC (U01CA105417), NCRR/BIRN (U24 RR021760)"
])
metadata.append([])
trait_list = []
for trait in table_rows:
trait_name, dataset_name, _hash = trait.split(":")
trait_ob = create_trait(name=trait_name, dataset_name=dataset_name)
trait_ob = retrieve_trait_info(trait_ob,
trait_ob.dataset,
get_qtl_info=True)
trait_list.append(trait_ob)
table_headers = [
'Index', 'URL', 'Species', 'Group', 'Dataset', 'Record ID', 'Symbol',
'Description', 'ProbeTarget', 'PubMed_ID', 'Chr', 'Mb', 'Alias',
'Gene_ID', 'Homologene_ID', 'UniGene_ID', 'Strand_Probe',
'Probe_set_specificity', 'Probe_set_BLAT_score',
'Probe_set_BLAT_Mb_start', 'Probe_set_BLAT_Mb_end', 'QTL_Chr',
'QTL_Mb', 'Locus_at_Peak', 'Max_LRS', 'P_value_of_MAX',
'Mean_Expression'
]
traits_by_group = sort_traits_by_group(trait_list)
file_list = []
for group in list(traits_by_group.keys()):
group_traits = traits_by_group[group]
buff = io.StringIO()
writer = csv.writer(buff)
csv_rows = []
sample_headers = []
for sample in group_traits[0].dataset.group.samplelist:
sample_headers.append(sample)
sample_headers.append(sample + "_SE")
full_headers = table_headers + sample_headers
for metadata_row in metadata:
writer.writerow(metadata_row)
csv_rows.append(full_headers)
for i, trait in enumerate(group_traits):
if getattr(trait, "symbol", None):
trait_symbol = getattr(trait, "symbol")
elif getattr(trait, "abbreviation", None):
trait_symbol = getattr(trait, "abbreviation")
else:
trait_symbol = "N/A"
row_contents = [
i + 1, "https://genenetwork.org/show_trait?trait_id=" +
str(trait.name) + "&dataset=" + str(trait.dataset.name),
trait.dataset.group.species, trait.dataset.group.name,
trait.dataset.name, trait.name, trait_symbol,
getattr(trait, "description_display", "N/A"),
getattr(trait, "probe_target_description", "N/A"),
getattr(trait, "pubmed_id", "N/A"),
getattr(trait, "chr", "N/A"),
getattr(trait, "mb", "N/A"), trait.alias_fmt,
getattr(trait, "geneid", "N/A"),
getattr(trait, "homologeneid", "N/A"),
getattr(trait, "unigeneid", "N/A"),
getattr(trait, "strand_probe", "N/A"),
getattr(trait, "probe_set_specificity", "N/A"),
getattr(trait, "probe_set_blat_score", "N/A"),
getattr(trait, "probe_set_blat_mb_start", "N/A"),
getattr(trait, "probe_set_blat_mb_end", "N/A"),
getattr(trait, "locus_chr", "N/A"),
getattr(trait, "locus_mb", "N/A"),
getattr(trait, "locus", "N/A"),
getattr(trait, "lrs", "N/A"),
getattr(trait, "pvalue", "N/A"),
getattr(trait, "mean", "N/A")
]
for sample in trait.dataset.group.samplelist:
if sample in trait.data:
row_contents += [
trait.data[sample].value, trait.data[sample].variance
]
else:
row_contents += ["x", "x"]
csv_rows.append(row_contents)
csv_rows = list(
map(list, itertools.zip_longest(*[row for row in csv_rows])))
writer.writerows(csv_rows)
csv_data = buff.getvalue()
buff.close()
file_name = group + "_traits.csv"
file_list.append([file_name, csv_data])
return file_list
def test_retrieve_trait_info_with_empty_trait_info(self, requests_mock):
"""Empty trait info"""
requests_mock.return_value = TestNilResponse()
with self.assertRaises(KeyError):
retrieve_trait_info(trait=mock.MagicMock(),
dataset=mock.MagicMock())
def test_retrieve_trait_info_with_empty_dataset(self):
"""Test that an exception is raised when dataset is empty"""
with self.assertRaises(AssertionError):
retrieve_trait_info(trait=mock.MagicMock(), dataset={})