diff options
| -rw-r--r-- | wqflask/base/data_set.py | 2 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_gn3_api.py | 115 |
2 files changed, 7 insertions, 110 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py index 62afdb63..d31161ec 100644 --- a/wqflask/base/data_set.py +++ b/wqflask/base/data_set.py @@ -672,6 +672,8 @@ class DataSet: return results def get_probeset_data(self, sample_list=None, trait_ids=None): + + # improvement of get trait data--->>> if sample_list: self.samplelist = sample_list diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py index 3e1ce1dc..eb986655 100644 --- a/wqflask/wqflask/correlation/correlation_gn3_api.py +++ b/wqflask/wqflask/correlation/correlation_gn3_api.py @@ -27,13 +27,11 @@ def create_target_this_trait(start_vars): return (this_dataset, this_trait, target_dataset, sample_data) - -def test_process_data(this_trait,dataset,start_vars): +def test_process_data(this_trait, dataset, start_vars): """test function for bxd,all and other sample data""" corr_samples_group = start_vars["corr_samples_group"] - primary_samples = dataset.group.samplelist if dataset.group.parlist != None: primary_samples += dataset.group.parlist @@ -51,10 +49,12 @@ def test_process_data(this_trait,dataset,start_vars): if corr_samples_group == 'samples_other': primary_samples = [x for x in primary_samples if x not in ( dataset.group.parlist + dataset.group.f1list)] - sample_data = process_samples(start_vars, list(this_trait.data.keys()), primary_samples) + sample_data = process_samples(start_vars, list( + this_trait.data.keys()), primary_samples) return sample_data + def process_samples(start_vars, sample_names, excluded_samples=None): """process samples""" sample_data = {} @@ -149,7 +149,7 @@ def fetch_sample_data(start_vars, this_trait, this_dataset, target_dataset): # sample_data = test_process_data(this_trait,this_dataset,start_vars) - if target_dataset.type =="ProbeSet": + if target_dataset.type == "ProbeSet": # pass target_dataset.get_probeset_data(list(sample_data.keys())) else: @@ -238,7 +238,6 @@ def compute_correlation(start_vars, method="pearson"): "target_dataset": start_vars['corr_dataset'], "return_results": corr_return_results} - return correlation_data @@ -299,107 +298,3 @@ def get_tissue_correlation_input(this_trait, trait_symbol_dict): } return (primary_tissue_data, target_tissue_data) return None - - -def generate_corr_data(corr_results, target_dataset): - counter = 0 - results_list = [] - for (index, trait_corr) in enumerate(corr_results): - trait_name = list(trait_corr.keys())[0] - trait = create_trait(dataset=target_dataset, - name=trait_name) - - trait_corr_data = trait_corr[trait_name] - - if trait.view == False: - continue - results_dict = {} - results_dict['index'] = index + 1 - results_dict['trait_id'] = trait.name - results_dict['dataset'] = trait.dataset.name - # results_dict['hmac'] = hmac.data_hmac( - # '{}:{}'.format(trait.name, trait.dataset.name)) - if target_dataset.type == "ProbeSet": - results_dict['symbol'] = trait.symbol - results_dict['description'] = "N/A" - results_dict['location'] = trait.location_repr - results_dict['mean'] = "N/A" - results_dict['additive'] = "N/A" - if bool(trait.description_display): - results_dict['description'] = trait.description_display - if bool(trait.mean): - results_dict['mean'] = f"{float(trait.mean):.3f}" - try: - results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = trait.LRS_location_repr - if bool(trait.additive): - results_dict['additive'] = f"{float(trait.additive):.3f}" - results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}" - results_dict['lit_corr'] = "--" - results_dict['tissue_corr'] = "--" - results_dict['tissue_pvalue'] = "--" - tissue_corr = trait_corr_data.get('tissue_corr',0) - lit_corr = trait_corr_data.get('lit_corr',0) - if bool(lit_corr): - results_dict['lit_corr'] = f"{float(trait_corr_data.get('lit_corr',0)):.3f}" - if bool(tissue_corr): - results_dict['tissue_corr'] = f"{float(trait_corr_data.get('tissue_corr',0)):.3f}" - results_dict['tissue_pvalue'] = f"{float(trait_corr_data.get('tissue_pvalue',0)):.3e}" - elif target_dataset.type == "Publish": - results_dict['abbreviation_display'] = "N/A" - results_dict['description'] = "N/A" - results_dict['mean'] = "N/A" - results_dict['authors_display'] = "N/A" - results_dict['additive'] = "N/A" - if for_api: - results_dict['pubmed_id'] = "N/A" - results_dict['year'] = "N/A" - else: - results_dict['pubmed_link'] = "N/A" - results_dict['pubmed_text'] = "N/A" - - if bool(trait.abbreviation): - results_dict['abbreviation_display'] = trait.abbreviation - if bool(trait.description_display): - results_dict['description'] = trait.description_display - if bool(trait.mean): - results_dict['mean'] = f"{float(trait.mean):.3f}" - if bool(trait.authors): - authors_list = trait.authors.split(',') - if len(authors_list) > 6: - results_dict['authors_display'] = ", ".join( - authors_list[:6]) + ", et al." - else: - results_dict['authors_display'] = trait.authors - if bool(trait.pubmed_id): - if for_api: - results_dict['pubmed_id'] = trait.pubmed_id - results_dict['year'] = trait.pubmed_text - else: - results_dict['pubmed_link'] = trait.pubmed_link - results_dict['pubmed_text'] = trait.pubmed_text - try: - results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = trait.LRS_location_repr - if bool(trait.additive): - results_dict['additive'] = f"{float(trait.additive):.3f}" - results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}" - else: - results_dict['location'] = trait.location_repr - results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}" - - results_list.append(results_dict) - - return results_list - - |