diff options
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_gn3_api.py | 126 |
1 files changed, 45 insertions, 81 deletions
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py index 3c21a850..b56c09d8 100644 --- a/wqflask/wqflask/correlation/correlation_gn3_api.py +++ b/wqflask/wqflask/correlation/correlation_gn3_api.py @@ -1,61 +1,55 @@ """module that calls the gn3 api's to do the correlation """ import json -import requests -import time + from wqflask.correlation import correlation_functions from base import data_set + from base.trait import create_trait from base.trait import retrieve_sample_data -# gn3 lib + from gn3.computations.correlations import compute_all_sample_correlation from gn3.computations.correlations import map_shared_keys_to_values -from gn3.computations.correlations import compute_all_tissue_correlation from gn3.computations.correlations import compute_all_lit_correlation from gn3.computations.correlations import experimental_compute_all_tissue_correlation from gn3.db_utils import database_connector -GN3_CORRELATION_API = "http://127.0.0.1:8202/api/correlation" + +def create_target_this_trait(start_vars): + """this function creates the required trait and target dataset for correlation""" + + this_dataset = data_set.create_dataset(dataset_name=start_vars['dataset']) + target_dataset = data_set.create_dataset( + dataset_name=start_vars['corr_dataset']) + this_trait = create_trait(dataset=this_dataset, + name=start_vars['trait_id']) + sample_data = () + return (this_dataset, this_trait, target_dataset, sample_data) def process_samples(start_vars, sample_names, excluded_samples=None): - """process samples method""" + """process samples""" sample_data = {} if not excluded_samples: excluded_samples = () - sample_vals_dict = json.loads(start_vars["sample_vals"]) - for sample in sample_names: if sample not in excluded_samples: val = sample_vals_dict[sample] if not val.strip().lower() == "x": sample_data[str(sample)] = float(val) - return sample_data -def create_target_this_trait(start_vars): - """this function creates the required trait and target dataset for correlation""" - - this_dataset = data_set.create_dataset(dataset_name=start_vars['dataset']) - target_dataset = data_set.create_dataset( - dataset_name=start_vars['corr_dataset']) +def sample_for_trait_lists(corr_results, target_dataset, + this_trait, this_dataset, start_vars): + """interface function for correlation on top results""" - this_trait = create_trait(dataset=this_dataset, - name=start_vars['trait_id']) - - # target_dataset.get_trait_data(list(self.sample_data.keys())) - - # this_trait = retrieve_sample_data(this_trait, this_dataset) - sample_data = () - return (this_dataset, this_trait, target_dataset, sample_data) - - -def sample_for_trait_lists(corr_results, target_dataset, this_trait, this_dataset, start_vars): sample_data = process_samples( start_vars, this_dataset.group.samplelist) target_dataset.get_trait_data(list(sample_data.keys())) + # should filter target traits from here + _corr_results = corr_results this_trait = retrieve_sample_data(this_trait, this_dataset) @@ -69,65 +63,55 @@ def sample_for_trait_lists(corr_results, target_dataset, this_trait, this_datase this_trait=this_trait_data, target_dataset=results) - return correlation_results -def tissue_for_trait_lists(corr_results, this_dataset, target_dataset, this_trait): - trait_lists = dict([(list(corr_result)[0], True) - for corr_result in corr_results]) +def tissue_for_trait_lists(corr_results, this_dataset, this_trait): + """interface function for doing tissue corr_results on trait_list""" + # trait_lists = dict([(list(corr_result)[0], True) + # for corr_result in corr_results]) + trait_lists = {list(corr_results)[0]: 1 for corr_result in corr_results} traits_symbol_dict = this_dataset.retrieve_genes("Symbol") traits_symbol_dict = dict({trait_name: symbol for ( trait_name, symbol) in traits_symbol_dict.items() if trait_lists.get(trait_name)}) primary_tissue_data, target_tissue_data = get_tissue_correlation_input( this_trait, traits_symbol_dict) corr_results = experimental_compute_all_tissue_correlation( - primary_tissue_dict=primary_tissue_data, target_tissues_data=target_tissue_data, corr_method="pearson") + primary_tissue_dict=primary_tissue_data, + target_tissues_data=target_tissue_data, + corr_method="pearson") return corr_results def compute_correlation(start_vars, method="pearson"): """compute correlation for to call gn3 api""" - import time + # pylint: disable-msg=too-many-locals corr_type = start_vars['corr_type'] (this_dataset, this_trait, target_dataset, sample_data) = create_target_this_trait(start_vars) - # cor_results = compute_correlation(start_vars) - method = start_vars['corr_sample_method'] - + _corr_return_results = start_vars.get("corr_return_results", 100) corr_input_data = {} if corr_type == "sample": - + sample_data = process_samples( start_vars, this_dataset.group.samplelist) - initial_time = time.time() target_dataset.get_trait_data(list(sample_data.keys())) this_trait = retrieve_sample_data(this_trait, this_dataset) - print("Creating target dataset and trait took", time.time()-initial_time) - - this_trait_data = { "trait_sample_data": sample_data, "trait_id": start_vars["trait_id"] } - initial_time = time.time() results = map_shared_keys_to_values( target_dataset.samplelist, target_dataset.trait_data) correlation_results = compute_all_sample_correlation(corr_method=method, this_trait=this_trait_data, target_dataset=results) - print("doing sample correlation took", time.time()-initial_time) - # other_results = tissue_for_trait_lists( - # correlation_results, this_dataset, target_dataset, this_trait) - # requests_url = f"{GN3_CORRELATION_API}/sample_x/{method}" - return correlation_results - elif corr_type == "tissue": trait_symbol_dict = this_dataset.retrieve_genes("Symbol") primary_tissue_data, target_tissue_data = get_tissue_correlation_input( @@ -137,50 +121,33 @@ def compute_correlation(start_vars, method="pearson"): "primary_tissue": primary_tissue_data, "target_tissues_dict": target_tissue_data } - initial_time = time.time() - correlation_results = experimental_compute_all_tissue_correlation(primary_tissue_dict=corr_input_data["primary_tissue"], - target_tissues_data=corr_input_data[ - "target_tissues_dict"], - corr_method=method) - print("computing tissue took >>>>", time.time()-initial_time) - # sample_results = sample_for_trait_lists( - # correlation_results, target_dataset, this_trait, this_dataset, start_vars) - return correlation_results + correlation_results = experimental_compute_all_tissue_correlation( + primary_tissue_dict=corr_input_data["primary_tissue"], + target_tissues_data=corr_input_data[ + "target_tissues_dict"], + corr_method=method + + ) elif corr_type == "lit": (this_trait_geneid, geneid_dict, species) = do_lit_correlation( - this_trait, this_dataset, target_dataset) + this_trait, this_dataset) conn, _cursor_object = database_connector() - initial_time = time.time() with conn: - - lit_corr_results = compute_all_lit_correlation( + correlation_results = compute_all_lit_correlation( conn=conn, trait_lists=list(geneid_dict.items()), species=species, gene_id=this_trait_geneid) - return lit_corr_results - # requests_url = f"{GN3_CORRELATION_API}/lit_corr/{species}/{this_trait_geneid}" - # corr_input_data = geneid_dict - # corr_results = requests.post(requests_url, json=corr_input_data) - - # data = corr_results.json() - - # return data + return correlation_results -def do_lit_correlation(this_trait, this_dataset, target_dataset): +def do_lit_correlation(this_trait, this_dataset): + """function for fetching lit inputs""" geneid_dict = this_dataset.retrieve_genes("GeneId") - # - print("CALLING THE LIT CORRELATION HERE") species = this_dataset.group.species.lower() - - this_trait_geneid = this_trait.geneid - this_trait_gene_data = { - this_trait.name: this_trait_geneid - } - - return (this_trait_geneid, geneid_dict, species) + trait_geneid = this_trait.geneid + return (trait_geneid, geneid_dict, species) def get_tissue_correlation_input(this_trait, trait_symbol_dict): @@ -190,7 +157,6 @@ def get_tissue_correlation_input(this_trait, trait_symbol_dict): if this_trait.symbol.lower() in primary_trait_tissue_vals_dict: primary_trait_tissue_values = primary_trait_tissue_vals_dict[this_trait.symbol.lower( )] - corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( symbol_list=list(trait_symbol_dict.values())) primary_tissue_data = { @@ -202,7 +168,5 @@ def get_tissue_correlation_input(this_trait, trait_symbol_dict): "trait_symbol_dict": trait_symbol_dict, "symbol_tissue_vals_dict": corr_result_tissue_vals_dict } - return (primary_tissue_data, target_tissue_data) - return None |