diff options
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_gn3_api.py | 39 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/rust_correlation.py | 121 |
2 files changed, 78 insertions, 82 deletions
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py index 6df4eafe..1a375501 100644 --- a/wqflask/wqflask/correlation/correlation_gn3_api.py +++ b/wqflask/wqflask/correlation/correlation_gn3_api.py @@ -194,46 +194,13 @@ def compute_correlation(start_vars, method="pearson", compute_all=False): method -- Correlation method to be used (pearson, spearman, or bicor) compute_all -- Include sample, tissue, and literature correlations (when applicable) """ - # pylint: disable-msg=too-many-locals + from wqflask.correlation.rust_correlation import compute_correlation_rust corr_type = start_vars['corr_type'] - method = start_vars['corr_sample_method'] corr_return_results = int(start_vars.get("corr_return_results", 100)) - corr_input_data = {} - - from wqflask.correlation.rust_correlation import compute_correlation_rust - rust_correlation_results = compute_correlation_rust( - start_vars, corr_type, method, corr_return_results) - correlation_results = rust_correlation_results["correlation_results"] - - if corr_type == "lit":# elif corr_type == "lit": - (this_dataset, this_trait, target_dataset, - sample_data) = create_target_this_trait(start_vars) - target_dataset_type = target_dataset.type - this_dataset_type = this_dataset.type - (this_trait_geneid, geneid_dict, species) = do_lit_correlation( - this_trait, this_dataset) - - conn = database_connector() - with conn: - correlation_results = compute_all_lit_correlation( - conn=conn, trait_lists=list(geneid_dict.items()), - species=species, gene_id=this_trait_geneid) - - correlation_results = correlation_results[0:corr_return_results] - - if (compute_all): - correlation_results = compute_corr_for_top_results( - start_vars, correlation_results, this_trait, this_dataset, - target_dataset, corr_type) - - return { - "correlation_results": correlation_results, - "this_trait": this_trait.name, - "target_dataset": start_vars['corr_dataset'], - "return_results": corr_return_results - } + return compute_correlation_rust( + start_vars, corr_type, method, corr_return_results, compute_all) def compute_corr_for_top_results(start_vars, diff --git a/wqflask/wqflask/correlation/rust_correlation.py b/wqflask/wqflask/correlation/rust_correlation.py index 4a22af72..b4435887 100644 --- a/wqflask/wqflask/correlation/rust_correlation.py +++ b/wqflask/wqflask/correlation/rust_correlation.py @@ -69,62 +69,91 @@ def merge_results(dict_a: dict, dict_b: dict, dict_c: dict) -> list[dict]: } return [__merge__(tname, tcorrs) for tname, tcorrs in dict_a.items()] +def __compute_sample_corr__( + start_vars: dict, corr_type: str, method: str, n_top: int, + target_trait_info: tuple): + """Compute the sample correlations""" + (this_dataset, this_trait, target_dataset, sample_data) = target_trait_info + all_samples = json.loads(start_vars["sample_vals"]) + sample_data = get_sample_corr_data( + sample_type=start_vars["corr_samples_group"], all_samples=all_samples, + dataset_samples=this_dataset.group.all_samples_ordered()) + target_dataset.get_trait_data(list(sample_data.keys())) + + target_data = [] + for (key, val) in target_dataset.trait_data.items(): + lts = [key] + [str(x) for x in val] + r = ",".join(lts) + target_data.append(r) + + + return run_correlation( + target_data, list(sample_data.values()), method, ",", corr_type, + n_top) + +def __compute_tissue_corr__( + start_vars: dict, corr_type: str, method: str, n_top: int, + target_trait_info: tuple): + """Compute the tissue correlations""" + (this_dataset, this_trait, target_dataset, sample_data) = target_trait_info + trait_symbol_dict = this_dataset.retrieve_genes("Symbol") + corr_result_tissue_vals_dict = get_trait_symbol_and_tissue_values( + symbol_list=list(trait_symbol_dict.values())) -def compute_correlation_rust( - start_vars: dict, corr_type: str, method: str = "pearson", - n_top: int = 500): - """function to compute correlation""" - - (this_dataset, this_trait, target_dataset, - sample_data) = create_target_this_trait(start_vars) - - if corr_type == "sample": - - all_samples = json.loads(start_vars["sample_vals"]) - sample_data = get_sample_corr_data(sample_type=start_vars["corr_samples_group"], - all_samples=all_samples, - dataset_samples=this_dataset.group.all_samples_ordered()) + data = parse_tissue_corr_data( + symbol_name=this_trait.symbol, + symbol_dict=get_trait_symbol_and_tissue_values( + symbol_list=[this_trait.symbol]), + dataset_symbols=trait_symbol_dict, + dataset_vals=corr_result_tissue_vals_dict) - target_dataset.get_trait_data(list(sample_data.keys())) + if data: + return run_correlation(data[1], data[0], method, ",", "tissue") + return {} - target_data = [] - for (key, val) in target_dataset.trait_data.items(): - lts = [key] + [str(x) for x in val] - r = ",".join(lts) - target_data.append(r) +def __compute_lit_corr__( + start_vars: dict, corr_type: str, method: str, n_top: int, + target_trait_info: tuple): + """Compute the literature correlations""" + (this_dataset, this_trait, target_dataset, sample_data) = target_trait_info + target_dataset_type = target_dataset.type + this_dataset_type = this_dataset.type + (this_trait_geneid, geneid_dict, species) = do_lit_correlation( + this_trait, this_dataset) + with database_connector() as conn: + return compute_all_lit_correlation( + conn=conn, trait_lists=list(geneid_dict.items()), + species=species, gene_id=this_trait_geneid) + return {} - results = run_correlation( - target_data, list(sample_data.values()), method, ",", corr_type, - n_top) +def compute_correlation_rust( + start_vars: dict, corr_type: str, method: str = "pearson", + n_top: int = 500, compute_all: bool = False): + """function to compute correlation""" + target_trait_info = create_target_this_trait(start_vars) + (this_dataset, this_trait, target_dataset, sample_data) = ( + target_trait_info) + + corr_type_fns = { + "sample": __compute_sample_corr__, + "tissue": __compute_tissue_corr__, + "lit": __compute_lit_corr__ + } + results = corr_type_fns[corr_type]( + start_vars, corr_type, method, n_top, target_trait_info) + top_tissue_results = {} + top_lit_results = {} + if compute_all: # example compute of compute both correlation - top_tissue_results = compute_top_n_tissue(this_dataset,this_trait,results,method) + top_tissue_results = compute_top_n_tissue( + this_dataset,this_trait,results,method) top_lit_results = compute_top_n_lit(results,this_dataset,this_trait) - # merging the results - results = merge_results(results, top_tissue_results, top_lit_results) - - if corr_type == "tissue": - - trait_symbol_dict = this_dataset.retrieve_genes("Symbol") - corr_result_tissue_vals_dict = get_trait_symbol_and_tissue_values( - symbol_list=list(trait_symbol_dict.values())) - - data = parse_tissue_corr_data(symbol_name=this_trait.symbol, - symbol_dict=get_trait_symbol_and_tissue_values( - symbol_list=[this_trait.symbol] - ), - dataset_symbols=trait_symbol_dict, - dataset_vals=corr_result_tissue_vals_dict) - - if data: - results = merge_results( - run_correlation(data[1], data[0], method, ",", "tissue"), - {}, {}) - return { - "correlation_results": results, + "correlation_results": merge_results( + results, top_tissue_results, top_lit_results), "this_trait": this_trait.name, "target_dataset": start_vars['corr_dataset'], "return_results": n_top |