def run_query(gcp_project_id, x, y, cohort_id_array, program_array, project_id_array, cohort_table_id, log_transform):
from bq_data_access.v2.data_access import FeatureVectorBigQueryBuilder
from bq_data_access.bigquery_cohorts import BigQueryCohortStorageSettings
from bq_data_access.v2.plot_data_support import get_merged_feature_vectors
from google_helpers.bigquery.service_v2 import BigQueryServiceSupport
# Verify the program set
# ----------------------
program_set = get_bq_program_set(program_array)
logger.info("Selected programs: {}".format(program_set))
if len(program_set) == 0:
logger.info("No programs set. Please include at least one program.")
sys_exit(0)
# Verify the cohort ID array
# --------------------------
if len(cohort_id_array) == 0:
logger.info("No cohort IDs set. Please include at least one cohort ID.")
sys_exit(0)
# Verify the project ID array
# ---------------------------
if len(project_id_array) == 0:
logger.info("No project IDs set. Please include at least one project ID.")
sys_exit(0)
cohort_settings = BigQueryCohortStorageSettings.build_from_full_table_id(cohort_table_id)
bqss = BigQueryServiceSupport.build_from_application_default()
fvb = FeatureVectorBigQueryBuilder(gcp_project_id, cohort_settings, bqss)
data = get_merged_feature_vectors(fvb, x, y, None, cohort_id_array, log_transform, project_id_array, program_set=program_set)
def oncoprint_view_data(request):
try:
gene_list_str = request.GET.get('gene_list', None)
gene_array = gene_list_str.split(',')
genomic_build = request.GET.get('genomic_build', None)
cohort_id_param_array = request.GET.getlist('cohort_id', None)
if not is_valid_genomic_build(genomic_build):
return JsonResponse({'error': 'Invalid genomic build'}, status=400)
cohort_id_array = []
for cohort_id in cohort_id_param_array:
try:
cohort_id = int(cohort_id)
cohort_id_array.append(cohort_id)
except Exception as e:
return JsonResponse({'error': 'Invalid cohort parameter'}, status=400)
if len(cohort_id_array) == 0:
return JsonResponse({'error': 'No cohorts specified'}, status=400)
program_set = get_program_set_for_oncoprint(cohort_id_array)
confirmed_project_ids, user_only_study_ids = get_confirmed_project_ids_for_cohorts(cohort_id_array)
# Only samples in projects from a data type's valid programs should be queried
projects_this_program_set = Project.objects.filter(id__in=confirmed_project_ids,program__in=program_set).values_list('id', flat=True)
if not len(program_set):
return JsonResponse(
{'message': "The chosen cohorts do not contain samples from programs with Gene Mutation data."})
query_template = """
#standardSQL
SELECT cs.case_barcode, sm.Hugo_Symbol, sm.Alteration, sm.Type
FROM (
SELECT case_barcode
FROM `{cohort_table}`
WHERE cohort_id IN ({cohort_id_list})
AND (project_id IS NULL{project_clause})
GROUP BY case_barcode
) cs
LEFT JOIN (
SELECT
case_barcode, Hugo_Symbol,
CASE
WHEN Protein_position IS NOT NULL AND Protein_position NOT LIKE '-/%' THEN
CONCAT(
COALESCE(REGEXP_EXTRACT(Amino_acids,r'^([A-Za-z*\-]+)'),'-'),
COALESCE(REGEXP_EXTRACT(Protein_position,r'^([0-9]+)'), '-'),
CASE
WHEN Variant_Classification IN ('Frame_Shift_Del', 'Frame_Shift_Ins') OR {conseq_col} LIKE '%frameshift%' THEN '_fs'
WHEN Variant_Classification IN ('Splice_Site', 'Splice_Region') THEN '_splice'
WHEN Amino_acids LIKE '%/%' THEN REGEXP_EXTRACT(Amino_acids,r'^.*/([A-Za-z*-]+)')
ELSE '-'
END
)
ELSE
CASE
WHEN {conseq_col} LIKE '%splice_%_variant%' THEN REGEXP_EXTRACT({conseq_col},r'^(splice_[^_]+_variant)')
WHEN {conseq_col} LIKE '%intron_variant%' THEN 'intron_variant'
WHEN Variant_Classification = 'IGR' THEN 'Intergenic'
ELSE Variant_Classification
END
END AS Alteration,
CASE
WHEN (Amino_acids IS NOT NULL AND REGEXP_EXTRACT(Amino_acids,r'^.*/([A-Za-z*-]+)$') = '*') OR Variant_Classification IN ('Frame_Shift_Del', 'Frame_Shift_Ins', 'Splice_Site', 'Splice_Region') THEN 'TRUNC'
WHEN Variant_Classification = 'Nonsense_Mutation' AND {conseq_col} LIKE 'stop_gained%' THEN 'TRUNC'
WHEN Variant_Classification = 'Nonstop_Mutation' OR (Variant_Classification = 'Missense_Mutation' AND Variant_Type IN ('DEL','INS')) OR (Variant_Classification = 'Translation_Start_Site') THEN 'MISSENSE'
WHEN (Variant_Classification = 'Missense_Mutation' AND Variant_Type IN ('ONP','SNP', 'TNP')) OR (Variant_Classification IN ('In_Frame_Del','In_Frame_Ins')) OR {conseq_col} LIKE '%inframe%' THEN 'INFRAME'
WHEN Variant_Classification IN ("RNA","IGR", "3\'UTR","3\'Flank","5\'UTR","5\'Flank") THEN
CASE
WHEN {conseq_col} LIKE '%intergenic%' THEN 'INTERGENIC'
WHEN {conseq_col} LIKE '%regulatory%' THEN 'REGULATORY'
WHEN {conseq_col} LIKE '%miRNA%' THEN 'miRNA'
WHEN {conseq_col} LIKE '%transcript%' THEN 'TRANSCRIPT'
WHEN {conseq_col} LIKE '%downstream%' THEN 'DOWNSTREAM'
WHEN {conseq_col} LIKE '%upstream%' THEN 'UPSTREAM'
ELSE UPPER(Variant_Classification)
END
ELSE UPPER(Variant_Classification)
END AS Type
FROM `{bq_data_project_id}.{dataset_name}.{table_name}`
WHERE Variant_Classification NOT IN ('Silent') {filter_clause}
AND case_barcode IN (
SELECT case_barcode
FROM `{cohort_table}`
WHERE cohort_id IN ({cohort_id_list})
AND (project_id IS NULL{project_clause})
GROUP BY case_barcode
)
GROUP BY case_barcode, Hugo_Symbol, Alteration, Type
ORDER BY case_barcode
) sm
ON sm.case_barcode = cs.case_barcode
;
"""
project_id_stmt = ""
if projects_this_program_set and len(projects_this_program_set):
project_id_stmt = ', '.join([str(project_id) for project_id in projects_this_program_set])
project_clause = " OR project_id IN ({})".format(project_id_stmt) if projects_this_program_set else ""
gene_list_stm = ''
if gene_array is not None:
gene_list_stm = ', '.join('\'{0}\''.format(gene) for gene in gene_array)
filter_clause = "AND Hugo_Symbol IN ({})".format(gene_list_stm) if gene_list_stm != "" else ""
cohort_id_list = ', '.join([str(cohort_id) for cohort_id in cohort_id_array])
cohort_table_id = "{project_name}.{dataset_id}.{table_id}".format(
project_name=settings.BIGQUERY_PROJECT_ID,
dataset_id=settings.BIGQUERY_COHORT_DATASET_ID,
table_id=settings.BIGQUERY_COHORT_TABLE_ID)
bq_table_info = BQ_MOLECULAR_ATTR_TABLES['TCGA'][genomic_build]
somatic_mut_query = query_template.format(
bq_data_project_id = settings.BIGQUERY_DATA_PROJECT_ID,
dataset_name=bq_table_info['dataset'],
table_name=bq_table_info['table'],
conseq_col=("one_consequence" if genomic_build == "hg38" else 'consequence'),
cohort_table=cohort_table_id,
filter_clause=filter_clause,
cohort_id_list=cohort_id_list,
project_clause=project_clause
)
somatic_mut_query_job = BigQuerySupport.insert_query_job(somatic_mut_query)
plot_data = []
genes_with_no_cnvr = []
# Build the CNVR features
for gene in gene_array:
feature = build_feature_ids(
"CNVR", {'value_field': 'segment_mean', 'gene_name': gene, 'genomic_build': genomic_build}
)
if not feature or not len(feature):
logger.warn("[WARNING] No internal feature ID found for CNVR, gene {}, build {}.".format(gene,genomic_build))
genes_with_no_cnvr.append(gene)
continue
feature = feature[0]['internal_feature_id']
fvb = FeatureVectorBigQueryBuilder.build_from_django_settings(BigQueryServiceSupport.build_from_django_settings())
data = get_merged_feature_vectors(fvb, feature, None, None, cohort_id_array, None, projects_this_program_set, program_set=program_set)['items']
if data and len(data):
for item in data:
# 01A are tumor samples, which is what we want
if item['sample_id'].split('-')[-1] == '01A':
seg_mean = float(item['x'])
if seg_mean > 0.112 or seg_mean < -0.112:
cnvr_result = "AMP" if seg_mean > 1 else "GAIN" if seg_mean > 0.62 else "HOMDEL" if seg_mean < -1 else "HETLOSS"
plot_data.append("{}\t{}\t{}\t{}".format(item['case_id'],gene,cnvr_result,"CNA"))
attempts = 0
job_is_done = BigQuerySupport.check_job_is_done(somatic_mut_query_job)
while attempts < settings.BQ_MAX_ATTEMPTS and not job_is_done:
job_is_done = BigQuerySupport.check_job_is_done(somatic_mut_query_job)
sleep(1)
attempts += 1
if job_is_done:
results = BigQuerySupport.get_job_results(somatic_mut_query_job['jobReference'])
#Only add plot_data if gene info is not missing
if results and len(results) > 0:
for row in results:
if row['f'][1]['v']:
plot_data.append("{}\t{}\t{}\t{}".format(str(row['f'][0]['v']),str(row['f'][1]['v']),str(row['f'][2]['v']),str(row['f'][3]['v'])))
if len(plot_data):
plot_message = \
'' if not genes_with_no_cnvr \
else "No internal feature ID found for CNVR, gene [{}], build {}."\
.format(', '.join(genes_with_no_cnvr), genomic_build)
return JsonResponse({
'plot_data': plot_data,
'gene_list': gene_array,
'bq_tables': ["{bq_data_project_id}:{dataset_name}.{table_name}".format(
bq_data_project_id=settings.BIGQUERY_DATA_PROJECT_ID,
dataset_name=bq_table_info['dataset'],
table_name=bq_table_info['table'])],
'plot_message': plot_message,
})
else:
return JsonResponse(
{'message': "The chosen genes and cohorts do not contain any samples with Gene Mutation data."})
except Exception as e:
logger.error("[ERROR] In oncoprint_view_data: ")
logger.exception(e)
return JsonResponse({'Error': str(e)}, status=500)
def data_access_for_plot(request):
"""
Used by the web application.
"""
try:
logTransform = None
ver = request.GET.get('ver', '1')
x_id = request.GET.get('x_id', None)
y_id = request.GET.get('y_id', None)
c_id = request.GET.get('c_id', None)
try:
# TODO Use jsonschema to validate logTransform object
logTransform = json.loads(request.GET.get('log_transform', None))
except Exception as e:
logger.warn("[WARNING] Log transform parameter not supplied")
logTransform = None
cohort_id_array = request.GET.getlist('cohort_id', None)
# Check that all requested feature identifiers are valid. Do not check for y_id if it is not
# supplied in the request.
feature_ids_to_check = [x_id]
if c_id is not None:
feature_ids_to_check.append(c_id)
if y_id is not None:
feature_ids_to_check.append(y_id)
valid_features = get_feature_id_validity_for_array(
feature_ids_to_check)
for feature_id, is_valid in valid_features:
if not is_valid:
logging.error(
"Invalid internal feature ID '{}'".format(feature_id))
raise Exception('Feature Not Found')
# Gives the user data handler a chance to map e.g. "v2:USER:343:58901" to "v2:CLIN:case_barcode"
x_id = _feature_converter(x_id)
y_id = _feature_converter(y_id)
c_id = _feature_converter(c_id)
# Get the project IDs these cohorts' samples come from
confirmed_study_ids, user_only_study_ids = get_confirmed_project_ids_for_cohorts(
cohort_id_array)
bqss = BigQueryServiceSupport.build_from_django_settings()
fvb = FeatureVectorBigQueryBuilder.build_from_django_settings(bqss)
# By extracting info from the cohort, we get the NAMES of the public projects
# we need to access (public projects have unique name tags, e.g. tcga).
program_set = get_public_program_name_set_for_cohorts(cohort_id_array)
# We need to do this for cohorts that contain samples found in user data projects,
# where those projects are extension of public data. This is because the cohorts
# only reference the user project, but if we are plotting against pubic data, we
# have to know which public programs we need to look at.
prog_set_extended = get_extended_public_program_name_set_for_user_extended_projects(
confirmed_study_ids)
program_set.update(prog_set_extended)
# Check to see if these programs have data for the requested vectors; if not, there's no reason to plot
features_without_program_data = []
for id in [x_id, y_id, c_id]:
if id:
type = id.split(':')[1].lower()
plot_type = FEATURE_ID_TO_TYPE_MAP[
type] if type in FEATURE_ID_TO_TYPE_MAP else None
if plot_type:
programs = FeatureDataTypeHelper.get_supported_programs_from_data_type(
plot_type)
valid_programs = set(programs).intersection(program_set)
if not len(valid_programs):
features_without_program_data.append(
FeatureDataTypeHelper.get_proper_feature_type_name(
plot_type))
if len(features_without_program_data):
return JsonResponse({
'message':
"The chosen cohorts do not contain programs with data for these features: {}."
.format(", ".join(features_without_program_data))
})
user_programs = get_user_program_id_set_for_user_only_projects(
user_only_study_ids)
# Fix for #2381: confirmed_study_ids MUST ALWAYS contain the public dataset project IDs, because that's how we
# enable older cohorts which didn't store project IDs (check for NULL) against ones where we did require the
# project ID
if len(user_programs):
program_set.update(user_programs)
confirmed_study_ids += user_only_study_ids
data = get_merged_feature_vectors(fvb,
x_id,
y_id,
c_id,
cohort_id_array,
logTransform,
confirmed_study_ids,
program_set=program_set)
# Annotate each data point with cohort information
add_cohort_info_to_merged_vectors(data, x_id, y_id, c_id,
cohort_id_array)
# convert to display strings where needed (eg. categoricals stored as indicies rather than strings)
programs_by_project = {}
preformatted_vals = {}
for item in data['items']:
programs = []
for project in item['project']:
# Fetch the program if we don't know it already
if project not in programs_by_project:
programs_by_project[project] = Project.objects.get(
id=project).program.id
programs.append(programs_by_project[project])
for program in programs:
if program not in preformatted_vals:
preformatted_vals[program] = fetch_metadata_value_set(
program)
if x_id is not None and x_id.split(':')[-1] in preformatted_vals[program] and item['x'] in \
preformatted_vals[program][x_id.split(':')[-1]]['values']:
item['x'] = preformatted_vals[program][x_id.split(
':')[-1]]['values'][item['x']]['displ_value']
if y_id is not None and y_id.split(':')[-1] in preformatted_vals[program] and item['y'] in \
preformatted_vals[program][y_id.split(':')[-1]]['values']:
item['y'] = preformatted_vals[program][y_id.split(
':')[-1]]['values'][item['y']]['displ_value']
if c_id is not None and c_id.split(':')[-1] in preformatted_vals[program] and item['c'] in \
preformatted_vals[program][c_id.split(':')[-1]]['values']:
item['c'] = preformatted_vals[program][c_id.split(
':')[-1]]['values'][item['c']]['displ_value']
return JsonResponse(data)
except Exception as e:
logger.error("[ERROR] In data access for plot: ")
logger.exception(e)
return JsonResponse({'error': str(e)}, status=500)
def data_access_for_plot(request):
"""
Used by the web application.
"""
try:
logTransform = None
ver = request.GET.get('ver', '1')
x_id = request.GET.get('x_id', None)
y_id = request.GET.get('y_id', None)
c_id = request.GET.get('c_id', None)
try:
logTransform = json.loads(request.GET.get('log_transform', None))
except Exception as e:
print >> sys.stdout, "[WARNING] Log transform parameter not supplied"
logger.warn("[WARNING] Log transform parameter not supplied")
logTransform = None
cohort_id_array = request.GET.getlist('cohort_id', None)
# Check that all requested feature identifiers are valid. Do not check for y_id if it is not
# supplied in the request.
feature_ids_to_check = [x_id]
if c_id is not None:
feature_ids_to_check.append(c_id)
if y_id is not None:
feature_ids_to_check.append(y_id)
valid_features = get_feature_id_validity_for_array(
feature_ids_to_check)
for feature_id, is_valid in valid_features:
logging.info((feature_id, is_valid))
if not is_valid:
logging.error(
"Invalid internal feature ID '{}'".format(feature_id))
raise Exception('Feature Not Found')
# Get the project IDs these cohorts' samples come from
confirmed_study_ids = get_confirmed_project_ids_for_cohorts(
cohort_id_array)
bqss = BigQueryServiceSupport.build_from_django_settings()
fvb = FeatureVectorBigQueryBuilder.build_from_django_settings(bqss)
program_set = get_public_program_name_set_for_cohorts(cohort_id_array)
data = get_merged_feature_vectors(fvb,
x_id,
y_id,
None,
cohort_id_array,
logTransform,
confirmed_study_ids,
program_set=program_set)
# Annotate each data point with cohort information
add_cohort_info_to_merged_vectors(data, x_id, y_id, c_id,
cohort_id_array)
return JsonResponse(data)
except Exception as e:
print >> sys.stdout, traceback.format_exc()
logger.exception(e)
return JsonResponse({'error': str(e)}, status=500)