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| author | Alexander Kabui | 2021-06-20 09:11:03 +0300 |
|---|---|---|
| committer | Alexander Kabui | 2021-06-20 09:11:03 +0300 |
| commit | 123ad47af288d6b94f0354a0abd5bc669bc988d4 (patch) | |
| tree | 999eb41c1fab3d443215c0e4a8533abecf64f9af /gn3/computations/correlations.py | |
| parent | 75801d83c8302b48051d413490e6ce2a0b8ff01f (diff) | |
| parent | d653a635d0efd2291754c18f51d31f91a1c0a25c (diff) | |
| download | genenetwork3-123ad47af288d6b94f0354a0abd5bc669bc988d4.tar.gz | |
merge main
Diffstat (limited to 'gn3/computations/correlations.py')
| -rw-r--r-- | gn3/computations/correlations.py | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py index eae7ae4..bc738a7 100644 --- a/gn3/computations/correlations.py +++ b/gn3/computations/correlations.py @@ -69,8 +69,8 @@ pearson,spearman and biweight mid correlation return value is rho and p_value "spearman": scipy.stats.spearmanr } use_corr_method = corr_mapping.get(corr_method, "spearman") - corr_coeffient, p_val = use_corr_method(primary_values, target_values) - return (corr_coeffient, p_val) + corr_coefficient, p_val = use_corr_method(primary_values, target_values) + return (corr_coefficient, p_val) def compute_sample_r_correlation(trait_name, corr_method, trait_vals, @@ -85,13 +85,13 @@ def compute_sample_r_correlation(trait_name, corr_method, trait_vals, if num_overlap > 5: - (corr_coeffient, p_value) =\ + (corr_coefficient, p_value) =\ compute_corr_coeff_p_value(primary_values=sanitized_traits_vals, target_values=sanitized_target_vals, corr_method=corr_method) - if corr_coeffient is not None: - return (trait_name, corr_coeffient, p_value, num_overlap) + if corr_coefficient is not None: + return (trait_name, corr_coefficient, p_value, num_overlap) return None @@ -145,10 +145,10 @@ def compute_all_sample_correlation(this_trait, for sample_correlation in results: if sample_correlation is not None: - (trait_name, corr_coeffient, p_value, + (trait_name, corr_coefficient, p_value, num_overlap) = sample_correlation corr_result = { - "corr_coeffient": corr_coeffient, + "corr_coefficient": corr_coefficient, "p_value": p_value, "num_overlap": num_overlap } @@ -156,7 +156,7 @@ def compute_all_sample_correlation(this_trait, corr_results.append({trait_name: corr_result}) return sorted( corr_results, - key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coeffient"])) + key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coefficient"])) def benchmark_compute_all_sample(this_trait, @@ -179,12 +179,12 @@ def benchmark_compute_all_sample(this_trait, trait_vals=this_vals, target_samples_vals=target_vals) if sample_correlation is not None: - (trait_name, corr_coeffient, + (trait_name, corr_coefficient, p_value, num_overlap) = sample_correlation else: continue corr_result = { - "corr_coeffient": corr_coeffient, + "corr_coefficient": corr_coefficient, "p_value": p_value, "num_overlap": num_overlap } @@ -200,20 +200,20 @@ def tissue_correlation_for_trait( 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 - output -> List containing Dicts with corr_coefficient value,P_value and + output -> List containing Dicts with corr_coefficient value, P_value and also the tissue numbers is len(primary) == len(target) """ # ax :todo assertion that length one one target tissue ==primary_tissue - (tissue_corr_coeffient, + (tissue_corr_coefficient, p_value) = compute_corr_p_value(primary_values=primary_tissue_vals, target_values=target_tissues_values, corr_method=corr_method) tiss_corr_result = {trait_id: { - "tissue_corr": tissue_corr_coeffient, + "tissue_corr": tissue_corr_coefficient, "tissue_number": len(primary_tissue_vals), "tissue_p_val": p_value}} |