diff options
| -rw-r--r-- | wqflask/wqflask/correlation/show_corr_results.py | 346 |
1 files changed, 208 insertions, 138 deletions
diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py index 5da8a6b9..56378d27 100644 --- a/wqflask/wqflask/correlation/show_corr_results.py +++ b/wqflask/wqflask/correlation/show_corr_results.py @@ -44,13 +44,10 @@ def set_template_vars(start_vars, correlation_data): target_dataset_ob = create_dataset(correlation_data['target_dataset']) correlation_data['target_dataset'] = target_dataset_ob.as_monadic_dict().data - - table_json = correlation_json_for_table(correlation_data, - correlation_data['this_trait'], - correlation_data['this_dataset'], - target_dataset_ob) - - correlation_data['table_json'] = table_json + correlation_data['table_json'] = correlation_json_for_table( + start_vars, + correlation_data, + target_dataset_ob.as_monadic_dict().data) if target_dataset_ob.type == "ProbeSet": filter_cols = [7, 6] @@ -69,151 +66,224 @@ def set_template_vars(start_vars, correlation_data): return correlation_data -def correlation_json_for_table(start_vars, correlation_data, this_trait, this_dataset, target_dataset_ob): - """Return JSON data for use with the DataTable in the correlation result page +def apply_filters(trait, target_trait, target_dataset, **filters): + def __p_val_filter__(p_lower, p_upper): - Keyword arguments: - correlation_data -- Correlation results - this_trait -- Trait being correlated against a dataset, as a dict - this_dataset -- Dataset of this_trait, as a monadic dict - target_dataset_ob - Target dataset, as a Dataset ob - """ - this_trait = correlation_data['this_trait'] - this_dataset = correlation_data['this_dataset'] - target_dataset = target_dataset_ob.as_monadic_dict().data + return not (p_lower <= float(trait.get("corr_coefficient",0.0)) <= p_upper) + + def __min_filter__(min_expr): + if (target_dataset['type'] in ["ProbeSet", "Publish"] and target_trait['mean']): + return (min_expr != None) and (float(target_trait['mean']) < min_expr) + + return False - corr_results = correlation_data['correlation_results'] - results_list = [] + def __location_filter__(location_type, location_chr, + min_location_mb, max_location_mb): - new_traits_metadata = {} + if target_dataset["type"] in ["ProbeSet", "'Geno"] and location_type == "gene": - dataset_metadata = correlation_data["traits_metadata"] + return ( + ((location_chr!=None) and (target_trait["chr"]!=location_chr)) + or + ((min_location_mb!= None) and ( + float(target_trait['mb']) < min_location_mb) + ) - min_expr = get_float(start_vars, 'min_expr') - p_range_lower = get_float(start_vars, 'p_range_lower', -1.0) - p_range_upper = get_float(start_vars, 'p_range_upper', 1.0) + or + ((max_location_mb != None) and + (float(target_trait['mb']) > float(max_location_mb) + )) - if ('loc_chr' in start_vars and - 'min_loc_mb' in start_vars and - 'max_loc_mb' in start_vars): + ) + elif target_dataset["type"] in ["ProbeSet", "Publish"]: + + return ((location_chr!=None) and (target_trait["lrs_chr"] != location_chr) + or + ((min_location_mb != None) and ( + float(target_trait['lrs_mb']) < float(min_location_mb))) + or + ((max_location_mb != None) and ( + float(target_trait['lrs_mb']) > float(max_location_mb)) + ) + + ) + + return True + + # check if one of the condition is not met i.e One is True + + return (__p_val_filter__( + filters.get("p_range_lower"), + filters.get("p_range_upper") + ) + or + ( + __min_filter__( + filters.get("min_expr") + ) + ) + or + __location_filter__( + filters.get("location_type"), + filters.get("location_chr"), + filters.get("min_location_mb"), + filters.get("max_location_mb") + + + ) + ) + + +def get_user_filters(start_vars): + (min_expr, p_min, p_max) = ( + get_float(start_vars, 'min_expr'), + get_float(start_vars, 'p_range_lower', -1.0), + get_float(start_vars, 'p_range_upper', 1.0) + ) + + if all(keys in start_vars for keys in ["loc_chr", + "min_loc_mb", + "max_location_mb"]): + + location_chr = get_string(start_vars, "loc_chr") + min_location_mb = get_int(start_vars, "min_loc_mb") + max_location_mb = get_int(start_vars, "max_loc_mb") - location_chr = get_string(start_vars, 'loc_chr') - min_location_mb = get_int(start_vars, 'min_loc_mb') - max_location_mb = get_int(start_vars, 'max_loc_mb') else: location_chr = min_location_mb = max_location_mb = None - for i, trait_dict in enumerate(corr_results): - trait_name = list(trait_dict.keys())[0] - trait = trait_dict[trait_name] + return { + + "min_expr": min_expr, + "p_range_lower": p_min, + "p_range_upper": p_max, + "location_chr": location_chr, + "location_type": start_vars['location_type'], + "min_location_mb": min_location_mb, + "max_location_mb": max_location_mb + + } + +def generate_table_metadata(all_traits, dataset_metadata, dataset_obj): + + def __fetch_trait_data__(trait, dataset_obj): + target_trait_ob = create_trait(dataset=dataset_obj, + name=trait, + get_qtl_info=True) + return jsonable(target_trait_ob, dataset_obj) + + metadata = [__fetch_trait_data__(trait, dataset_obj) for + trait in (all_traits ^ dataset_metadata.keys())] + return (dataset_metadata | ({trait["name"]: trait for trait in metadata})) + + +def populate_table(dataset_metadata, target_dataset, this_dataset, corr_results, filters): + + def __populate_trait__(idx, trait): + + trait_name = list(trait.keys())[0] target_trait = dataset_metadata.get(trait_name) - if target_trait is None: - target_trait_ob = create_trait(dataset=target_dataset_ob, - name=trait_name, - get_qtl_info=True) - target_trait = jsonable(target_trait_ob, target_dataset_ob) - new_traits_metadata[trait_name] = target_trait - - if (float(trait.get('corr_coefficient',0.0)) >= p_range_lower and - float(trait.get('corr_coefficient',0.0)) <= p_range_upper): - - if (target_dataset['type'] == "ProbeSet" or target_dataset['type'] == "Publish") and bool(target_trait['mean']): - if (min_expr != None) and (float(target_trait['mean']) < min_expr): - continue - - if start_vars['location_type'] == "gene" and (target_dataset['type'] == "ProbeSet" or target_dataset['type'] == "Geno"): - if location_chr != None and (target_trait['chr'] != location_chr): - continue - if (min_location_mb != None) and (float(target_trait['mb']) < float(min_location_mb)): - continue - if (max_location_mb != None) and (float(target_trait['mb']) > float(max_location_mb)): - continue - elif target_dataset['type'] == "ProbeSet" or target_dataset['type'] == "Publish": - if location_chr != None and (target_trait['lrs_chr'] != location_chr): - continue - if (min_location_mb != None) and (float(target_trait['lrs_mb']) < float(min_location_mb)): - continue - if (max_location_mb != None) and (float(target_trait['lrs_mb']) > float(max_location_mb)): - continue - else: - continue - else: - continue - - results_dict = {} - results_dict['index'] = i + 1 - results_dict['trait_id'] = target_trait['name'] - results_dict['dataset'] = target_dataset['name'] - results_dict['hmac'] = hmac.data_hmac( - '{}:{}'.format(target_trait['name'], target_dataset['name'])) - results_dict['sample_r'] = f"{float(trait.get('corr_coefficient',0.0)):.3f}" - results_dict['num_overlap'] = trait.get('num_overlap',0) - results_dict['sample_p'] = f"{float(trait.get('p_value',0)):.3e}" - if target_dataset['type'] == "ProbeSet": - results_dict['symbol'] = target_trait['symbol'] - results_dict['description'] = "N/A" - results_dict['location'] = target_trait['location'] - results_dict['mean'] = "N/A" - results_dict['additive'] = "N/A" - if bool(target_trait['description']): - results_dict['description'] = target_trait['description'] - if bool(target_trait['mean']): - results_dict['mean'] = f"{float(target_trait['mean']):.3f}" - try: - results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = target_trait['lrs_location'] - if bool(target_trait['additive']): - results_dict['additive'] = f"{float(target_trait['additive']):.3f}" - results_dict['lit_corr'] = "--" - results_dict['tissue_corr'] = "--" - results_dict['tissue_pvalue'] = "--" - if this_dataset['type'] == "ProbeSet": - if 'lit_corr' in trait: - results_dict['lit_corr'] = f"{float(trait['lit_corr']):.3f}" - if 'tissue_corr' in trait: - results_dict['tissue_corr'] = f"{float(trait['tissue_corr']):.3f}" - results_dict['tissue_pvalue'] = f"{float(trait['tissue_p_val']):.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" - results_dict['pubmed_link'] = "N/A" - results_dict['pubmed_text'] = target_trait["pubmed_text"] - - if bool(target_trait['abbreviation']): - results_dict['abbreviation_display'] = target_trait['abbreviation'] - if bool(target_trait['description']): - results_dict['description'] = target_trait['description'] - if bool(target_trait['mean']): - results_dict['mean'] = f"{float(target_trait['mean']):.3f}" - if bool(target_trait['authors']): - authors_list = target_trait['authors'].split(',') - if len(authors_list) > 6: + trait = trait[trait_name] + if not apply_filters(trait, target_trait, target_dataset, **filters): + results_dict = {} + results_dict['index'] = idx + 1 # + results_dict['trait_id'] = target_trait['name'] + results_dict['dataset'] = target_dataset['name'] + results_dict['hmac'] = hmac.data_hmac( + '{}:{}'.format(target_trait['name'], target_dataset['name'])) + results_dict['sample_r'] = f"{float(trait.get('corr_coefficient',0.0)):.3f}" + results_dict['num_overlap'] = trait.get('num_overlap', 0) + results_dict['sample_p'] = f"{float(trait.get('p_value',0)):.3e}" + if target_dataset['type'] == "ProbeSet": + results_dict['symbol'] = target_trait['symbol'] + results_dict['description'] = "N/A" + results_dict['location'] = target_trait['location'] + results_dict['mean'] = "N/A" + results_dict['additive'] = "N/A" + if target_trait['description']: + results_dict['description'] = target_trait['description'] + if target_trait['mean']: + results_dict['mean'] = f"{float(target_trait['mean']):.3f}" + try: + results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}" + except: + results_dict['lod_score'] = "N/A" + results_dict['lrs_location'] = target_trait['lrs_location'] + if target_trait['additive']: + results_dict['additive'] = f"{float(target_trait['additive']):.3f}" + results_dict['lit_corr'] = "--" + results_dict['tissue_corr'] = "--" + results_dict['tissue_pvalue'] = "--" + if this_dataset['type'] == "ProbeSet": + if 'lit_corr' in trait: + results_dict['lit_corr'] = f"{float(trait['lit_corr']):.3f}" + if 'tissue_corr' in trait: + results_dict['tissue_corr'] = f"{float(trait['tissue_corr']):.3f}" + results_dict['tissue_pvalue'] = f"{float(trait['tissue_p_val']):.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" + results_dict['pubmed_link'] = "N/A" + results_dict['pubmed_text'] = target_trait["pubmed_text"] + + if target_trait["abbreviation"]: + results_dict = target_trait['abbreviation'] + + if target_trait["description"] == target_trait['description']: + results_dict['description'] = target_trait['description'] + + if target_trait["mean"]: + results_dict['mean'] = f"{float(target_trait['mean']):.3f}" + + if target_trait["authors"]: + authors_list = target_trait['authors'].split(',') results_dict['authors_display'] = ", ".join( - authors_list[:6]) + ", et al." - else: - results_dict['authors_display'] = target_trait['authors'] - if 'pubmed_id' in target_trait: - results_dict['pubmed_link'] = target_trait['pubmed_link'] - results_dict['pubmed_text'] = target_trait['pubmed_text'] - try: - results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = target_trait['lrs_location'] - if bool(target_trait['additive']): - results_dict['additive'] = f"{float(target_trait['additive']):.3f}" - else: - results_dict['location'] = target_trait['location'] + authors_list[:6]) + ", et al." if len(authors_list) > 6 else target_trait['authors'] + + if "pubmed_id" in target_trait: + results_dict['pubmed_link'] = target_trait['pubmed_link'] + results_dict['pubmed_text'] = target_trait['pubmed_text'] + try: + results_dict["lod_score"] = f"{float(target_trait['lrs_score']) / 4.61:.1f}" + except ValueError: + results_dict['lod_score'] = "N/A" + else: + results_dict['lrs_location'] = target_trait['lrs_location'] + + return results_dict + + return [__populate_trait__(idx, trait) + for (idx, trait) in enumerate(corr_results)] + + +def correlation_json_for_table(start_vars, correlation_data, target_dataset_ob): + """Return JSON data for use with the DataTable in the correlation result page + + Keyword arguments: + correlation_data -- Correlation results + this_trait -- Trait being correlated against a dataset, as a dict + this_dataset -- Dataset of this_trait, as a monadic dict + target_dataset_ob - Target dataset, as a Dataset ob + """ + this_dataset = correlation_data['this_dataset'] - results_list.append(results_dict) + traits = set() + for trait in correlation_data["correlation_results"]: + traits.add(list(trait)[0]) - return json.dumps(results_list) + dataset_metadata = generate_table_metadata(traits, + correlation_data["traits_metadata"], + target_dataset_ob) + return json.dumps([result for result in ( + populate_table(dataset_metadata=dataset_metadata, + target_dataset=target_dataset_ob.as_dict(), + this_dataset=correlation_data['this_dataset'], + corr_results=correlation_data['correlation_results'], + filters=get_user_filters(start_vars))) if result]) def get_formatted_corr_type(corr_type, corr_method): @@ -315,4 +385,4 @@ def get_header_fields(data_type, corr_method): 'N', 'Sample p(r)'] - return header_fields + return header_fields
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