def aggregated_statistics(self, request, pk):
""" Retrieve the aggregation percentage for associations meeting filters criteria for this gene. Check the FAQ for details on the filters. """
gene = elastic.load_gene_by_id(pk)
zoom = int(request.query_params.get('zoom', 0))
selected = bool(request.query_params.get('gene', '') == "1")
# last_el = [request.query_params.get('lastel', '')]
filters = _get_filter_from_params(request.query_params)
filters['chr'] = [gene['chr']]
filters['start'] = gene['positions']['gte'] - zoom
filters['end'] = gene['positions']['lte'] + zoom
if selected:
filters['start'] = gene['positions']['gte']
filters['end'] = gene['positions']['lte']
filters['gene_id'] = gene['name']
chr, maf, mac, type, annotations = elastic.get_aggregated_filtered_statistics(
filters)
chr_dict = _get_percentages_from_buckets(chr)
maf_dict = _get_percentages_from_buckets(maf)
mac_dict = _get_percentages_from_buckets(mac)
type_dict = _get_percentages_from_buckets(type)
annotations_dict = _get_percentages_from_buckets(annotations)
return Response({
'chromosomes': chr_dict,
'maf': maf_dict,
'mac': mac_dict,
'types': type_dict,
'annotations': annotations_dict
})
def associations(self, request, pk):
""" Return associations for the selected gene. Can add zoom and other filters. Check the FAQ for details on the filters. """
gene = elastic.load_gene_by_id(pk)
zoom = int(request.query_params.get('zoom', 0))
selected = bool(request.query_params.get('gene', '') == "1")
# last_el = [request.query_params.get('lastel', '')]
filters = _get_filter_from_params(request.query_params)
filters['chr'] = [gene['chr']]
filters['start'] = gene['positions']['gte'] - zoom
filters['end'] = gene['positions']['lte'] + zoom
if selected:
filters['start'] = gene['positions']['gte']
filters['end'] = gene['positions']['lte']
filters['gene_id'] = gene['name']
limit = self.paginator.get_limit(request)
offset = self.paginator.get_offset(request)
associations, count = elastic.load_filtered_top_associations(filters, offset, limit)
queryset = EsQuerySet(associations, count)
paginated_asso = self.paginate_queryset(queryset)
return self.get_paginated_response(paginated_asso)
def retrieve(self, request, pk):
""" Retrieve information about a specific gene """
gene = elastic.load_gene_by_id(pk)
return Response(gene)
def download_csv(self, request):
""" Prepare a csv file from the elasticsearch database and return it as a downloadable file through a celery task. Check the FAQ for details on the filters.
"""
# Load studies from regular db
import datetime
filters = _get_filter_from_params(request.query_params)
gene_id = request.query_params.getlist(
'gene_id'
) # We need to do this because we cannot solely rely on the annotations of the SNPs for gene-name
import os
export_folder = '%s/export' % settings.HDF5_FILE_PATH
if not os.path.isdir(export_folder):
os.mkdir(export_folder)
# Other download basenames:
download_name = "aragwas_associations"
if filters['study_id'] != []:
if isinstance(filters['study_id'], list):
filters['study_id'] = filters['study_id'][0]
study_name = Study.objects.get(pk=filters['study_id']).name
download_name = "{}_associations".format(study_name)
elif filters['phenotype_id'] != []:
phenotype_name = Phenotype.objects.get(
pk=filters['phenotype_id']).name
download_name = "{}_associations".format(phenotype_name)
if gene_id != []:
download_name = "{}_associations".format(
gene_id[0]) # Override previous ones
zoom = int(request.query_params.getlist('zoom')[0])
print(zoom)
gene = elastic.load_gene_by_id(gene_id[0])
filters['chr'] = [gene['chr']]
filters['start'] = gene['positions']['gte'] - zoom
filters['end'] = gene['positions']['lte'] + zoom
if _is_filter_whole_dataset(filters):
# download_name = "all_associations"
file_name = "%s/all_associations.csv" % (settings.HDF5_FILE_PATH)
chunk_size = 8192
response = StreamingHttpResponse(FileWrapper(
open(file_name, "rb"), chunk_size),
content_type="text/csv")
response['Content-Length'] = os.path.getsize(file_name)
else:
file_name = '%s/' % export_folder + str(
datetime.datetime.now()) + '.csv' # give it a unique name
opts = dict(doc_type='associations', output_file=file_name)
fn = download_es2csv(opts, filters)
# wait for file to be done
# Add filters to name:
if 'chr' in filters and len(filters['chr']) > 0 and len(
filters['chr']) < 5:
download_name += "_chr"
for c in filters['chr']:
download_name += "_{}".format(c)
if 'maf' in filters and len(filters['maf']) > 0 and len(
filters['maf']) < 4:
download_name += "_maf_filtered"
if 'annotation' in filters and len(
filters['annotation']) > 0 and len(
filters['annotation']) < 4:
download_name += "_annotation_filtered"
if 'type' in filters and len(filters['type']) == 1:
download_name += "_{}".format(filters['type'][0])
if 'significant' in filters:
if filters['significant'] == ['p']:
download_name += "_significant_permutation"
elif filters['significant'] == ['b']:
download_name += "_significant_bonferroni"
chunk_size = 8192
response = StreamingHttpResponse(FileWrapper(
open(file_name, "rb"), chunk_size),
content_type="text/csv")
response['Content-Length'] = os.path.getsize(file_name)
response['Content-Disposition'] = "attachment; filename={}.csv".format(
download_name)
return response
def retrieve(self, request, pk):
""" Retrieve information about a specific gene """
gene = elastic.load_gene_by_id(pk)
gene['ko_associations'] = elastic.load_gene_ko_associations(pk, return_only_significant=True)
return Response(gene)
