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"""Endpoints for running correlations"""
from flask import jsonify
from flask import Blueprint
from flask import request
from gn3.computations.correlations import compute_all_sample_correlation
from gn3.computations.correlations import compute_all_lit_correlation
from gn3.computations.correlations import compute_tissue_correlation
from gn3.computations.correlations import map_shared_keys_to_values
from gn3.db_utils import database_connector
from gn3.computations.partial_correlations import partial_correlations_entry
correlation = Blueprint("correlation", __name__)
@correlation.route("/sample_x/<string:corr_method>", methods=["POST"])
def compute_sample_integration(corr_method="pearson"):
"""temporary api to help integrate genenetwork2 to genenetwork3 """
correlation_input = request.get_json()
target_samplelist = correlation_input.get("target_samplelist")
target_data_values = correlation_input.get("target_dataset")
this_trait_data = correlation_input.get("trait_data")
results = map_shared_keys_to_values(target_samplelist, target_data_values)
correlation_results = compute_all_sample_correlation(corr_method=corr_method,
this_trait=this_trait_data,
target_dataset=results)
return jsonify(correlation_results)
@correlation.route("/sample_r/<string:corr_method>", methods=["POST"])
def compute_sample_r(corr_method="pearson"):
"""Correlation endpoint for computing sample r correlations\
api expects the trait data with has the trait and also the\
target_dataset data
"""
correlation_input = request.get_json()
# xtodo move code below to compute_all_sampl correlation
this_trait_data = correlation_input.get("this_trait")
target_dataset_data = correlation_input.get("target_dataset")
correlation_results = compute_all_sample_correlation(corr_method=corr_method,
this_trait=this_trait_data,
target_dataset=target_dataset_data)
return jsonify({
"corr_results": correlation_results
})
@correlation.route("/lit_corr/<string:species>/<int:gene_id>", methods=["POST"])
def compute_lit_corr(species=None, gene_id=None):
"""Api endpoint for doing lit correlation.results for lit correlation\
are fetched from the database this is the only case where the db\
might be needed for actual computing of the correlation results
"""
conn, _cursor_object = database_connector()
target_traits_gene_ids = request.get_json()
target_trait_gene_list = list(target_traits_gene_ids.items())
lit_corr_results = compute_all_lit_correlation(
conn=conn, trait_lists=target_trait_gene_list,
species=species, gene_id=gene_id)
conn.close()
return jsonify(lit_corr_results)
@correlation.route("/tissue_corr/<string:corr_method>", methods=["POST"])
def compute_tissue_corr(corr_method="pearson"):
"""Api endpoint fr doing tissue correlation"""
tissue_input_data = request.get_json()
primary_tissue_dict = tissue_input_data["primary_tissue"]
target_tissues_dict = tissue_input_data["target_tissues_dict"]
results = compute_tissue_correlation(primary_tissue_dict=primary_tissue_dict,
target_tissues_data=target_tissues_dict,
corr_method=corr_method)
return jsonify(results)
@correlation.route("/partial", methods=["POST"])
def partial_correlation():
def trait_fullname(trait):
return f"{trait['dataset']}::{trait['name']}"
args = request.get_json()
conn, _cursor_object = database_connector()
corr_results = partial_correlations_entry(
conn, trait_fullname(args["primary_trait"]),
tuple(trait_fullname(trait) for trait in args["control_traits"]),
args["method"], int(args["criteria"]), args["target_db"])
return make_response(
jsonify(corr_results),
400)
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