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| author | Alexander Kabui | 2021-05-03 10:43:07 +0300 |
|---|---|---|
| committer | Alexander Kabui | 2021-05-03 10:43:07 +0300 |
| commit | ef55d9769c50e12af6252f9fae78f5aa3bf42670 (patch) | |
| tree | 10c49b60fe0766fe8155210083fab1bdf13ec4e9 /gn3 | |
| parent | a1b1fdce9c92fd84e97310c79c17e7b1c74bff07 (diff) | |
| download | genenetwork3-ef55d9769c50e12af6252f9fae78f5aa3bf42670.tar.gz | |
minor fixes for tiss correlation tests and naming
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/computations/correlations.py | 28 |
1 files changed, 10 insertions, 18 deletions
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py index 3563530..065a1ed 100644 --- a/gn3/computations/correlations.py +++ b/gn3/computations/correlations.py @@ -226,6 +226,7 @@ def tissue_correlation_for_trait_list( primary_tissue_vals: List, target_tissues_values: List, corr_method: str, + trait_id: str, compute_corr_p_value: Callable = compute_corr_coeff_p_value) -> dict: """Given a primary tissue values for a trait and the target tissues values compute the correlation_cooeff and p value the input required are arrays @@ -241,13 +242,12 @@ def tissue_correlation_for_trait_list( target_values=target_tissues_values, corr_method=corr_method) - lit_corr_result = { + tiss_corr_result = {trait_id: { "tissue_corr": tissue_corr_coeffient, "tissue_number": len(primary_tissue_vals), - "p_value": p_value - } + "p_value": p_value}} - return lit_corr_result + return tiss_corr_result def fetch_lit_correlation_data( @@ -432,9 +432,9 @@ def process_trait_symbol_dict(trait_symbol_dict, symbol_tissue_vals_dict) -> Lis return traits_tissue_vals -def experimental_compute_all_tissue_correlation(primary_tissue_dict: dict, - target_tissues_data: dict, - corr_method: str): +def compute_tissue_correlation(primary_tissue_dict: dict, + target_tissues_data: dict, + corr_method: str): """Experimental function that uses multiprocessing\ for computing tissue correlation """ @@ -450,25 +450,17 @@ def experimental_compute_all_tissue_correlation(primary_tissue_dict: dict, processed_values = [] for target_tissue_obj in target_tissues_list: + trait_id = target_tissue_obj.get("trait_id") target_tissue_vals = target_tissue_obj.get("tissue_values") processed_values.append( - (primary_tissue_vals, target_tissue_vals, corr_method)) + (primary_tissue_vals, target_tissue_vals, corr_method, trait_id)) with multiprocessing.Pool() as pool: results = pool.starmap( tissue_correlation_for_trait_list, processed_values) for result in results: - tissue_result_dict = {"trait_name": result} - tissues_results.append(tissue_result_dict) - - # tissue_result = tissue_correlation_for_trait_list( - # primary_tissue_vals=primary_tissue_vals, - # target_tissues_values=target_tissue_vals, - # corr_method=corr_method) - - # tissue_result_dict = {trait_id: tissue_result} - # tissues_results.append(tissue_result_dict) + tissues_results.append(result) return sorted( tissues_results, |