def run(cancer_types, long_names, out_dir):
last_dataset = "000007S"
IDGenerator = id_generator.IDGenerator()
for cancer in cancer_types:
# get ref dataset id
data_id, last_dataset = IDGenerator.get_new_OEB_id(
"002", "D", last_dataset)
info = {
"_id":
"TCGA:2018-04-05_" + cancer + "_M",
"datalink": {
"uri": "https://portal.gdc.cancer.gov/",
"attrs": ["archive"],
"validation_date": "2018-04-05T00:00:00Z",
"status": "ok"
},
"type":
"metrics_reference",
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"visibility":
"public",
"version":
"unknown",
"name":
"Metrics Reference Dataset for " + long_names[cancer],
"description":
"List of genes (described by TCGA community) that can be used as 'gold standard' in "
+ long_names[cancer] + " benchmark ",
"dates": {
"creation": "2018-04-05T00:00:00Z",
"modification": "2018-04-05T14:00:00Z"
},
"depends_on": {
"rel_dataset_ids": [{
"dataset_id": "TCGA:2018-04-05_input",
}]
},
"_schema":
"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids": ["OEBC001"],
"dataset_contact_ids":
["Matthew.Bailey", "Eduard.Porta", "Collin.Tokheim"]
}
filename = "Dataset_Metrics_Ref_" + cancer + "_" + data_id + ".json"
# print filename
with open(out_dir + filename, 'w') as f:
json.dump(info,
f,
sort_keys=True,
indent=4,
separators=(',', ': '))
def run(cancer_types, mongo_ids, out_dir):
last_challenge = "0000000"
last_test_event = "0000000"
last_participant_dataset = "0000000"
last_tool = "0000008"
IDGenerator = id_generator.IDGenerator()
for cancer in cancer_types:
challenge_id, last_challenge = IDGenerator.get_new_OEB_id(
"002", "X", last_challenge)
for participant in os.listdir(
"/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"
):
#if participant is not in mongo, asign new temporary id
if participant in mongo_ids:
tool_id = mongo_ids[participant]
else:
tool_id, last_tool = IDGenerator.get_new_OEB_id(
"002", "T", last_tool)
#get test event id
Tevent_id, last_test_event = IDGenerator.get_new_OEB_id(
"002", "A", last_test_event)
# get participant dataset id
participant_data_id, last_participant_dataset = IDGenerator.get_new_OEB_id(
"002", "D", last_participant_dataset)
info = {
"_id":
"TCGA:2018-04-05_" + cancer + "_testEvent_" + participant,
"_schema":
"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/TestAction",
"action_type":
"TestEvent",
"tool_id":
"TCGA:" + participant,
"involved_datasets": [{
"dataset_id": "TCGA:2018-04-05_input",
"role": "incoming"
}, {
"dataset_id":
"TCGA:2018-04-05_" + cancer + "_P_" + participant,
"role":
"outgoing"
}],
"challenge_id":
"TCGA:2018-04-05_" + cancer,
"dates": {
"creation": "2018-04-05T00:00:00Z",
"reception": "2018-04-05T00:00:00Z"
},
"test_contact_ids":
["Matthew.Bailey", "Eduard.Porta", "Collin.Tokheim"]
}
# print info
filename = "TestEvent_" + cancer + "_" + participant + "_" + Tevent_id + ".json"
# print filename
with open(out_dir + filename, 'w') as f:
json.dump(info,
f,
sort_keys=True,
indent=4,
separators=(',', ': '))
def run(cancer_types, long_names, mongo_tool_ids, tool_contact, mongo_datRef_ids, out_dir):
## create dict that will store info about all combined cancer types
all_cancer_genes = {}
for participant in os.listdir("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"):
all_cancer_genes[participant] = []
last_challenge = "0000000"
last_event = "000007S"
last_participant_dataset = "0000000"
last_ref_dataset = "000007S"
last_tool = "0000008"
last_assessment_dataset = "000008R"
IDGenerator = id_generator.IDGenerator()
for cancer in cancer_types:
challenge_id, last_challenge = IDGenerator.get_new_OEB_id("002", "X", last_challenge)
# get metrics reference dataset id - incoming
ref_data_id, last_ref_dataset = IDGenerator.get_new_OEB_id("002", "D", last_ref_dataset)
data = pandas.read_csv("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/"+ cancer + ".txt",
comment="#", header=None)
gold_standard = data.iloc[:, 0].values
participants_datasets, all_cancer_genes = compute_metrics("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/", gold_standard, cancer, all_cancer_genes)
for participant in os.listdir("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"):
# if participant is not in mongo, asign new temporary id
if participant in mongo_tool_ids:
tool_id = mongo_tool_ids[participant]
else:
tool_id, last_tool = IDGenerator.get_new_OEB_id("002", "T", last_tool)
# get participant dataset id - incoming
participant_data_id, last_participant_dataset = IDGenerator.get_new_OEB_id("002", "D", last_participant_dataset)
#get data-uri value of the 2 metrics
metric1 = participants_datasets[participant][0]
metric2 = participants_datasets[participant][1]
#print metrics1 assesment file
# get assessment dataset id for metric 1
A_data_id, last_assessment_dataset = IDGenerator.get_new_OEB_id("002", "D", last_assessment_dataset)
info = {
"_id":"TCGA:2018-04-05_" + cancer + "_A_TPR_" + participant,
"description":"Assessment dataset for applying Metric 'True Positive Rate' to " + participant + " predictions in " + long_names[cancer],
"dates":{
"creation":"2018-04-05T00:00:00Z",
"modification":"2018-04-05T14:00:00Z"
},
"type":"assessment",
"visibility": "public",
"datalink":{
"inline_data": {"value": metric1}
},
"depends_on":{
"tool_id":"TCGA:" + participant,
"metrics_id":"TCGA:TPR",
"rel_dataset_ids":[
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_P_" + participant,
},
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_M",
}
]
},
"_schema":"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids":["OEBC001"],
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"version":"1",
"name":"Assesment of Metric TPR in " + participant,
"dataset_contact_ids":[
tool_contact[participant]
]
}
# print info
filename = "Dataset_assessment_" + cancer + "_" + participant + "_TPR_" + A_data_id + ".json"
print filename
with open(out_dir + filename, 'w') as f:
json.dump(info, f, sort_keys=True, indent=4, separators=(',', ': '))
# print metrics2 assessment file
# get assessment dataset id for metric 2
A_data_id, last_assessment_dataset = IDGenerator.get_new_OEB_id("002", "D", last_assessment_dataset)
info = {
"_id": "TCGA:2018-04-05_" + cancer + "_A_precision_" + participant,
"description": "Assessment dataset for applying Metric 'Positive Predictive Value' to " + participant + " predictions in " +
long_names[cancer],
"dates": {
"creation": "2018-04-05T00:00:00Z",
"modification": "2018-04-05T14:00:00Z"
},
"type": "assessment",
"visibility": "public",
"datalink": {
"inline_data": {"value": metric2}
},
"depends_on": {
"tool_id": "TCGA:" + participant,
"metrics_id": "TCGA:precision",
"rel_dataset_ids":[
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_P_" + participant,
},
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_M",
}
]
},
"_schema": "https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids":["OEBC001"],
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"version": "1",
"name": "Assesment of Metric precision-PPV in " + participant,
"dataset_contact_ids": [
tool_contact[participant]
]
}
# print info
filename = "Dataset_assessment_" + cancer + "_" + participant + "_precision_" + A_data_id + ".json"
print filename
with open(out_dir + filename, 'w') as f:
json.dump(info, f, sort_keys=True, indent=4, separators=(',', ': '))
get_metrics_across_all_cancers(all_cancer_genes, last_assessment_dataset, last_participant_dataset, last_tool, out_dir)
def get_metrics_across_all_cancers(all_cancer_genes, last_assessment_dataset, last_participant_dataset, last_tool, out_dir):
# plot chart for results across all cancer types
IDGenerator = id_generator.IDGenerator()
data = pandas.read_csv("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/ALL.txt",
comment="#", header=None)
gold_standard = data.iloc[:, 0].values
cancer = "ALL"
challenge_id = "OEBX002t00000Z"
ref_data_id = "OEBD002t00008R"
participants_datasets = {}
for participant in os.listdir("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"):
#get set of predicted genes store in all_cancer_genes
predicted_genes = all_cancer_genes[participant]
# TRUE POSITIVE RATE
overlapping_genes = set(predicted_genes).intersection(gold_standard)
TPR = len(overlapping_genes) / len(gold_standard)
# ACCURACY/ PRECISION
if len(predicted_genes) == 0:
acc = 0
else:
acc = len(overlapping_genes) / len(predicted_genes)
participants_datasets[participant] = [TPR, acc]
# if participant is not in mongo, asign new temporary id
if participant in mongo_tool_ids:
tool_id = mongo_tool_ids[participant]
else:
tool_id, last_tool = IDGenerator.get_new_OEB_id("002", "T", last_tool)
# get participant dataset id - incoming
participant_data_id, last_participant_dataset = IDGenerator.get_new_OEB_id("002", "D", last_participant_dataset)
# get data-uri value of the 2 metrics
metric1 = participants_datasets[participant][0]
metric2 = participants_datasets[participant][1]
# print metrics1 assesment file
# get assessment dataset id for metric 1
A_data_id, last_assessment_dataset = IDGenerator.get_new_OEB_id("002", "D", last_assessment_dataset)
info = {
"_id": "TCGA:2018-04-05_" + cancer + "_A_TPR_" + participant,
"description": "Assessment dataset for applying Metric 'True Positive Rate' to " + participant + " predictions in " +
long_names[cancer],
"dates": {
"creation": "2018-04-05T00:00:00Z",
"modification": "2018-04-05T14:00:00Z"
},
"type": "assessment",
"visibility": "public",
"datalink": {
"inline_data": {"value": metric1}
},
"depends_on": {
"tool_id": "TCGA:" + participant,
"metrics_id": "TCGA:TPR",
"rel_dataset_ids":[
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_P_" + participant,
},
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_M",
}
]
},
"_schema": "https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids":["OEBC001"],
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"version": "1",
"name": "Assesment of Metric TPR in " + participant,
"dataset_contact_ids": [
tool_contact[participant]
]
}
# print info
filename = "Dataset_assessment_" + cancer + "_" + participant + "_TPR_" + A_data_id + ".json"
print filename
with open(out_dir + filename, 'w') as f:
json.dump(info, f, sort_keys=True, indent=4, separators=(',', ': '))
# print metrics2 assessment file
# get assessment dataset id for metric 2
A_data_id, last_assessment_dataset = IDGenerator.get_new_OEB_id("002", "D", last_assessment_dataset)
info = {
"_id": "TCGA:2018-04-05_" + cancer + "_A_precision_" + participant,
"description": "Assessment dataset for applying Metric 'Positive Predictive Value' to " + participant + " predictions in " +
long_names[cancer],
"dates": {
"creation": "2018-04-05T00:00:00Z",
"modification": "2018-04-05T14:00:00Z"
},
"type": "assessment",
"visibility": "public",
"datalink": {
"inline_data": {"value": metric2}
},
"depends_on": {
"tool_id": "TCGA:" + participant,
"metrics_id": "TCGA:precision",
"rel_dataset_ids":[
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_P_" + participant,
},
{
"dataset_id":"TCGA:2018-04-05_" + cancer + "_M",
}
]
},
"_schema": "https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids":["OEBC001"],
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"version": "1",
"name": "Assesment of Metric precision-PPV in " + participant,
"dataset_contact_ids": [
tool_contact[participant]
]
}
# print info
filename = "Dataset_assessment_" + cancer + "_" + participant + "_precision_" + A_data_id + ".json"
print filename
with open(out_dir + filename, 'w') as f:
json.dump(info, f, sort_keys=True, indent=4, separators=(',', ': '))
def run(cancer_types, long_names, urls, out_dir):
IDGenerator = id_generator.IDGenerator()
last_used = "0000000"
for cancer in cancer_types:
# get schema alphanumeric id
challenge_id, last_used = IDGenerator.get_new_OEB_id(
"002", "X", last_used)
info = {
"_id":
"TCGA:2018-04-05_" + cancer,
"_schema":
"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Challenge",
"acronym":
cancer,
"name":
"Cancer Driver Genes Prediction Benchmark in " +
long_names[cancer],
"benchmarking_event_id":
"TCGA:2018-04-05",
"is_automated":
False,
"dates": {
"creation": "2018-04-05T00:00:00Z",
"modification": "2018-04-05T14:00:00Z",
"benchmark_start": "2018-04-05T05:00:00Z",
"benchmark_stop": "2018-04-05T02:00:00Z"
},
"metrics_categories": [{
"category":
"assessment",
"description":
"metrics used to benchmark the performance of cancer genes predictors in Challenge "
+ long_names[cancer] + ", generating the assessment datatseta",
"metrics": [{
"metrics_id": "TCGA:TPR",
"tool_id": "TCGA:compute_TPR"
}, {
"metrics_id": "TCGA:precision",
"tool_id": "TCGA:compute_precision"
}]
}, {
"category":
"aggregation",
"description":
"metrics used to aggregate the assessment data of all cancer genes predictors participating in challenge "
+ long_names[cancer] +
" in a consolidated Aggregation dataset",
"metrics": [{
"metrics_id": "TCGA:aggregation",
"tool_id": "TCGA:aggregate_benchmark"
}]
}],
"url":
urls[cancer],
"challenge_contact_ids":
["Matthew.Bailey", "Eduard.Porta", "Collin.Tokheim"],
"references": ["doi:10.1016/j.cell.2018.02.060"]
}
# print info
filename = "Challenge_" + cancer + "_" + challenge_id + ".json"
# print filename
with open(out_dir + filename, 'w') as f:
json.dump(info,
f,
sort_keys=True,
indent=4,
separators=(',', ': '))
def run(cancer_types, long_names, mongo_tool_ids, tool_contact, out_dir):
last_challenge = "0000000"
last_tool = "0000008"
last_assessment_dataset = "000008R"
last_challenge_dataset = "00000OB"
last_ref_dataset = "000007S"
IDGenerator = id_generator.IDGenerator()
for cancer in cancer_types:
challenge_id, last_challenge = IDGenerator.get_new_OEB_id("002", "X", last_challenge)
# get metrics reference dataset id - incoming
ref_data_id, last_ref_dataset = IDGenerator.get_new_OEB_id("002", "D", last_ref_dataset)
# get challenge dataset_id
challenge_data_id, last_challenge_dataset = IDGenerator.get_new_OEB_id("002", "D", last_challenge_dataset)
# generate array with all related datasets and object with participants metrics results
involved_datasets = []
inline_data = {
"visualization": { "type":"2D-plot",
"x_axis": "OEBM0020000002",
"y_axis": "OEBM0020000001"
},
"challenge_participants": []
}
for participant in os.listdir("/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"):
# if participant is not in mongo, asign new temporary id
if participant in mongo_tool_ids:
tool_id = mongo_tool_ids[participant]
else:
tool_id, last_tool = IDGenerator.get_new_OEB_id("002", "T", last_tool)
# get assessment dataset id for metric 1
A_data_id_TPR, last_assessment_dataset = IDGenerator.get_new_OEB_id("002", "D", last_assessment_dataset)
# get assessment dataset id for metric 2
A_data_id_precision, last_assessment_dataset = IDGenerator.get_new_OEB_id("002", "D", last_assessment_dataset)
# read files which containes metrics values
with io.open("out/assessment_datasets/Dataset_assessment_" + cancer + "_" + participant + "_TPR_" + A_data_id_TPR + ".json", mode='r', encoding="utf-8") as f:
assess_file = json.load(f)
metric1 = assess_file["datalink"]["inline_data"]["value"]
with io.open("out/assessment_datasets/Dataset_assessment_" + cancer + "_" + participant + "_precision_" + A_data_id_precision + ".json", mode='r', encoding="utf-8") as f:
assess_file = json.load(f)
metric2 = assess_file["datalink"]["inline_data"]["value"]
inline_data["challenge_participants"].append( {
"tool_id": "TCGA:" + participant,
"metric_x": metric1,
"metric_y": metric2,
})
###############################################################################
involved_datasets.append({
"dataset_id": "TCGA:2018-04-05_" + cancer + "_A_TPR_" + participant,
})
involved_datasets.append({
"dataset_id": "TCGA:2018-04-05_" + cancer + "_A_precision_" + participant,
})
# append reference and input datasets
involved_datasets.append({
"dataset_id": "TCGA:2018-04-05_input",
})
involved_datasets.append({
"dataset_id": "TCGA:2018-04-05_" + cancer + "_M",
})
info = {
"_id": "TCGA:2018-04-05_" + cancer + "_Aggregation",
"datalink":{
"inline_data": inline_data,
"schema_url": "https://raw.githubusercontent.com/inab/OpenEBench_scientific_visualizer/js/benchmarking_data_model/inline_data_visualizer.json"
},
"type":"aggregation",
"visibility": "public",
"version":"unknown",
"name":"Summary dataset for challenge: " + long_names[cancer],
"description":"Summary dataset with information about challenge " + long_names[cancer] + " (e.g. input/output datasets, metrics...) in participant " + participant,
"dates":{
"creation":"2018-04-05T00:00:00Z",
"modification":"2018-04-05T14:00:00Z"
},
"depends_on":{
"tool_id": "TCGA:aggregate_benchmark",
"rel_dataset_ids": involved_datasets,
},
"_schema":"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids":["OEBC001"],
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"dataset_contact_ids":[
"Eduard.Porta",
"Matthew.Bailey",
"Collin.Tokheim",
"Loris.Mularoni",
"Juri.Reimand",
"David.Tamborero",
"Nathan.Dees"
]
}
# print info
filename = "Dataset_Aggregation_" + cancer + "_" + challenge_data_id + ".json"
# print filename
with open(out_dir + filename, 'w') as f:
json.dump(info, f, sort_keys=True, indent=4, separators=(',', ': '))
def run(cancer_types, long_names, mongo_ids, tool_contact, download_urls,
out_dir):
last_challenge = "0000000"
last_participant_dataset = "0000000"
last_tool = "0000008"
IDGenerator = id_generator.IDGenerator()
for cancer in cancer_types:
challenge_id, last_challenge = IDGenerator.get_new_OEB_id(
"002", "X", last_challenge)
for participant in os.listdir(
"/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"
):
# if participant is not in mongo, assign new temporary id
if participant in mongo_ids:
tool_id = mongo_ids[participant]
else:
tool_id, last_tool = IDGenerator.get_new_OEB_id(
"002", "T", last_tool)
# get participant dataset id
participant_data_id, last_participant_dataset = IDGenerator.get_new_OEB_id(
"002", "D", last_participant_dataset)
info = {
"_id":
"TCGA:2018-04-05_" + cancer + "_P_" + participant,
"name":
"Cancer Driver Genes in " + long_names[cancer],
"description":
"List of Cancer Driver Genes predicted by tool " +
participant + " in " + long_names[cancer],
"dates": {
"creation": "2018-04-05T00:00:00Z",
"modification": "2018-04-05T14:00:00Z"
},
"datalink": {
"uri": download_urls[participant],
"attrs": ["archive"],
"validation_date": "2018-04-05T00:00:00Z",
"status": "ok"
},
"type":
"participant",
"visibility":
"public",
"_schema":
"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/Dataset",
"community_ids": ["OEBC001"],
"challenge_ids": ["TCGA:2018-04-05_" + cancer],
"depends_on": {
"tool_id": "TCGA:" + participant,
"rel_dataset_ids": [{
"dataset_id": "TCGA:2018-04-05_input",
}]
},
"version":
"unknown",
"dataset_contact_ids": [tool_contact[participant]]
}
# print info
filename = "Dataset_participant_" + cancer + "_" + participant + "_" + participant_data_id + ".json"
# print filename
with open(out_dir + filename, 'w') as f:
json.dump(info,
f,
sort_keys=True,
indent=4,
separators=(',', ': '))
def run(cancer_types, out_dir):
last_challenge = "0000000"
last_event = "00000NC"
last_tool = "0000008"
last_assessment_dataset = "000008R"
last_challenge_dataset = "00000OB"
IDGenerator = id_generator.IDGenerator()
for cancer in cancer_types:
challenge_id, last_challenge = IDGenerator.get_new_OEB_id(
"002", "X", last_challenge)
# get stat event id
Sevent_id, last_event = IDGenerator.get_new_OEB_id(
"002", "A", last_event)
#generate array with all incoming assessment datasets aun aoutgoing aggregation dataset
involved_datasets = []
for participant in os.listdir(
"/home/jgarrayo/PycharmProjects/TCGA_benchmark/input/participants"
):
involved_datasets.append({
"dataset_id":
"TCGA:2018-04-05_" + cancer + "_A_TPR_" + participant,
"role":
"incoming"
})
involved_datasets.append({
"dataset_id":
"TCGA:2018-04-05_" + cancer + "_A_precision_" + participant,
"role":
"incoming"
})
# append test action outgoing dataset
involved_datasets.append({
"dataset_id": "TCGA:2018-04-05_" + cancer + "_Aggregation",
"role": "outgoing"
})
info = {
"_id":
"TCGA:2018-04-05_" + cancer + "_do_aggregation",
"_schema":
"https://www.elixir-europe.org/excelerate/WP2/json-schemas/1.0/TestAction",
"tool_id":
"TCGA:aggregate_benchmark",
"action_type":
"AggregationEvent",
"involved_datasets":
involved_datasets,
"challenge_id":
"TCGA:2018-04-05_" + cancer,
"dates": {
"creation": "2018-04-05T00:00:00Z",
"reception": "2018-04-05T00:00:00Z"
},
"test_contact_ids":
["Matthew.Bailey", "Eduard.Porta", "Collin.Tokheim"]
}
# print info
filename = "AggregationEvent_" + cancer + "_" + Sevent_id + ".json"
# print filename
with open(out_dir + filename, 'w') as f:
json.dump(info,
f,
sort_keys=True,
indent=4,
separators=(',', ': '))
