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 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 set_template_vars(start_vars, correlation_data):
corr_type = start_vars['corr_type']
corr_method = start_vars['corr_sample_method']
if start_vars['dataset'] == "Temp":
this_dataset_ob = create_dataset(dataset_name="Temp",
dataset_type="Temp",
group_name=start_vars['group'])
else:
this_dataset_ob = create_dataset(dataset_name=start_vars['dataset'])
this_trait = create_trait(dataset=this_dataset_ob,
name=start_vars['trait_id'])
correlation_data['this_trait'] = jsonable(this_trait, this_dataset_ob)
correlation_data['this_dataset'] = this_dataset_ob.as_dict()
target_dataset_ob = create_dataset(correlation_data['target_dataset'])
correlation_data['target_dataset'] = target_dataset_ob.as_dict()
table_json = correlation_json_for_table(correlation_data,
correlation_data['this_trait'],
correlation_data['this_dataset'],
target_dataset_ob)
correlation_data['table_json'] = table_json
if target_dataset_ob.type == "ProbeSet":
filter_cols = [7, 6]
elif target_dataset_ob.type == "Publish":
filter_cols = [6, 0]
else:
filter_cols = [4, 0]
correlation_data['corr_method'] = corr_method
correlation_data['filter_cols'] = filter_cols
correlation_data['header_fields'] = get_header_fields(
target_dataset_ob.type, correlation_data['corr_method'])
correlation_data['formatted_corr_type'] = get_formatted_corr_type(
corr_type, corr_method)
return correlation_data
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 correlation_json_for_table(correlation_data, this_trait, this_dataset,
target_dataset_ob):
"""Return JSON data for use with the DataTable in the correlation result page
Keyword arguments:
correlation_data -- Correlation results
this_trait -- Trait being correlated against a dataset, as a dict
this_dataset -- Dataset of this_trait, as a dict
target_dataset_ob - Target dataset, as a Dataset ob
"""
this_trait = correlation_data['this_trait']
this_dataset = correlation_data['this_dataset']
target_dataset = target_dataset_ob.as_dict()
corr_results = correlation_data['correlation_results']
results_list = []
file_name = f"{target_dataset['name']}_metadata.json"
file_path = os.path.join(TMPDIR, file_name)
new_traits_metadata = {}
try:
with open(file_path, "r+") as file_handler:
dataset_metadata = json.load(file_handler)
except FileNotFoundError:
Path(file_path).touch(exist_ok=True)
dataset_metadata = {}
for i, trait_dict in enumerate(corr_results):
trait_name = list(trait_dict.keys())[0]
trait = trait_dict[trait_name]
target_trait = dataset_metadata.get(trait_name)
if target_trait is None:
target_trait_ob = create_trait(dataset=target_dataset_ob,
name=trait_name,
get_qtl_info=True)
target_trait = jsonable(target_trait_ob, target_dataset_ob)
new_traits_metadata[trait_name] = target_trait
if target_trait['view'] == False:
continue
results_dict = {}
results_dict['index'] = i + 1
results_dict['trait_id'] = target_trait['name']
results_dict['dataset'] = target_dataset['name']
results_dict['hmac'] = hmac.data_hmac('{}:{}'.format(
target_trait['name'], target_dataset['name']))
results_dict['sample_r'] = f"{float(trait['corr_coefficient']):.3f}"
results_dict['num_overlap'] = trait['num_overlap']
results_dict['sample_p'] = f"{float(trait['p_value']):.3e}"
if target_dataset['type'] == "ProbeSet":
results_dict['symbol'] = target_trait['symbol']
results_dict['description'] = "N/A"
results_dict['location'] = target_trait['location']
results_dict['mean'] = "N/A"
results_dict['additive'] = "N/A"
if bool(target_trait['description']):
results_dict['description'] = target_trait['description']
if bool(target_trait['mean']):
results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
try:
results_dict[
'lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
except:
results_dict['lod_score'] = "N/A"
results_dict['lrs_location'] = target_trait['lrs_location']
if bool(target_trait['additive']):
results_dict[
'additive'] = f"{float(target_trait['additive']):.3f}"
results_dict['lit_corr'] = "--"
results_dict['tissue_corr'] = "--"
results_dict['tissue_pvalue'] = "--"
if this_dataset['type'] == "ProbeSet":
if 'lit_corr' in trait:
results_dict[
'lit_corr'] = f"{float(trait['lit_corr']):.3f}"
if 'tissue_corr' in trait:
results_dict[
'tissue_corr'] = f"{float(trait['tissue_corr']):.3f}"
results_dict[
'tissue_pvalue'] = f"{float(trait['tissue_p_val']):.3e}"
elif target_dataset['type'] == "Publish":
results_dict['abbreviation_display'] = "N/A"
results_dict['description'] = "N/A"
results_dict['mean'] = "N/A"
results_dict['authors_display'] = "N/A"
results_dict['additive'] = "N/A"
results_dict['pubmed_link'] = "N/A"
results_dict['pubmed_text'] = "N/A"
if bool(target_trait['abbreviation']):
results_dict['abbreviation_display'] = target_trait[
'abbreviation']
if bool(target_trait['description']):
results_dict['description'] = target_trait['description']
if bool(target_trait['mean']):
results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
if bool(target_trait['authors']):
authors_list = target_trait['authors'].split(',')
if len(authors_list) > 6:
results_dict['authors_display'] = ", ".join(
authors_list[:6]) + ", et al."
else:
results_dict['authors_display'] = target_trait['authors']
if 'pubmed_id' in target_trait:
results_dict['pubmed_link'] = target_trait['pubmed_link']
results_dict['pubmed_text'] = target_trait['pubmed_text']
try:
results_dict[
'lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
except:
results_dict['lod_score'] = "N/A"
results_dict['lrs_location'] = target_trait['lrs_location']
if bool(target_trait['additive']):
results_dict[
'additive'] = f"{float(target_trait['additive']):.3f}"
else:
results_dict['location'] = target_trait['location']
results_list.append(results_dict)
if bool(new_traits_metadata):
# that means new traits exists
dataset_metadata.update(new_traits_metadata)
with open(file_path, "w+") as file_handler:
json.dump(dataset_metadata, file_handler)
return json.dumps(results_list)