diff options
Diffstat (limited to 'gn3/computations')
| -rw-r--r-- | gn3/computations/correlations.py | 51 |
1 files changed, 13 insertions, 38 deletions
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py index cd7d604..25dd26d 100644 --- a/gn3/computations/correlations.py +++ b/gn3/computations/correlations.py @@ -89,8 +89,6 @@ def compute_sample_r_correlation(trait_name, corr_method, trait_vals, target_values=sanitized_target_vals, corr_method=corr_method) - # xtodo check if corr_coefficient is None - # should use numpy.isNan scipy.isNan is deprecated if corr_coeffient is not None: return (trait_name, corr_coeffient, p_value, num_overlap) return None @@ -189,27 +187,7 @@ def benchmark_compute_all_sample(this_trait, return corr_results -def tissue_lit_corr_for_probe_type(corr_type: str, top_corr_results): - """Function that does either lit_corr_for_trait_list or tissue_corr _for_trait -list depending on whether both dataset and target_dataset are both set to -probet - - """ - corr_results = {"lit": 1} - if corr_type not in ("lit", "literature"): - corr_results["top_corr_results"] = top_corr_results - # run lit_correlation for the given top_corr_results - if corr_type == "tissue": - # run lit correlation the given top corr results - pass - if corr_type == "sample": - pass - # run sample r correlation for the given top results - - return corr_results - - -def tissue_correlation_for_trait_list( +def tissue_correlation_for_trait( primary_tissue_vals: List, target_tissues_values: List, corr_method: str, @@ -232,7 +210,7 @@ def tissue_correlation_for_trait_list( tiss_corr_result = {trait_id: { "tissue_corr": tissue_corr_coeffient, "tissue_number": len(primary_tissue_vals), - "p_value": p_value}} + "tissue_p_val": p_value}} return tiss_corr_result @@ -269,13 +247,13 @@ def fetch_lit_correlation_data( cursor.execute(query_formatter(query, *tuple(reversed(query_values)))) lit_corr_results = cursor.fetchone() - lit_results = (gene_id, lit_corr_results.val)\ + lit_results = (gene_id, lit_corr_results[1])\ if lit_corr_results else (gene_id, 0) return lit_results return (gene_id, 0) -def lit_correlation_for_trait_list( +def lit_correlation_for_trait( conn, target_trait_lists: List, species: Optional[str] = None, @@ -319,8 +297,6 @@ def map_to_mouse_gene_id(conn, species: Optional[str], gene_id: Optional[str]) -> Optional[str]: """Given a species which is not mouse map the gene_id\ to respective mouse gene id""" - # AK:xtodo move the code for checking nullity out of thing functions bug - # while method for string if None in (species, gene_id): return None if species == "mouse": @@ -339,11 +315,10 @@ def map_to_mouse_gene_id(conn, species: Optional[str], def compute_all_lit_correlation(conn, trait_lists: List, species: str, gene_id): - """Function that acts as an abstraction for lit_correlation_for_trait_list - - """ + """Function that acts as an abstraction for + lit_correlation_for_trait""" - lit_results = lit_correlation_for_trait_list( + lit_results = lit_correlation_for_trait( conn=conn, target_trait_lists=trait_lists, species=species, @@ -358,10 +333,9 @@ def compute_all_lit_correlation(conn, trait_lists: List, def compute_all_tissue_correlation(primary_tissue_dict: dict, target_tissues_data: dict, corr_method: str): - """Function acts as an abstraction for tissue_correlation_for_trait_list - required input are target tissue object and primary tissue trait target - tissues data contains the trait_symbol_dict and symbol_tissue_vals - + """Function acts as an abstraction for tissue_correlation_for_trait\ + required input are target tissue object and primary tissue trait\ + target tissues data contains the trait_symbol_dict and symbol_tissue_vals """ tissues_results = [] primary_tissue_vals = primary_tissue_dict["tissue_values"] @@ -372,7 +346,8 @@ def compute_all_tissue_correlation(primary_tissue_dict: dict, 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") - tissue_result = tissue_correlation_for_trait_list( + + tissue_result = tissue_correlation_for_trait( primary_tissue_vals=primary_tissue_vals, target_tissues_values=target_tissue_vals, trait_id=trait_id, @@ -425,7 +400,7 @@ def compute_tissue_correlation(primary_tissue_dict: dict, with multiprocessing.Pool(4) as pool: results = pool.starmap( - tissue_correlation_for_trait_list, processed_values) + tissue_correlation_for_trait, processed_values) for result in results: tissues_results.append(result) |