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| author | Alexander_Kabui | 2022-10-05 17:51:46 +0300 |
|---|---|---|
| committer | Alexander_Kabui | 2022-10-05 17:51:46 +0300 |
| commit | 721223e5879cb44d7186a2f32282371c7fb6090a (patch) | |
| tree | 37c5ed1354c13a5972751d5b0dd0a549f1a6b30d | |
| parent | 9bab022ec5d1db79fcbf837228ec6c6d9df81cc0 (diff) | |
| download | genenetwork2-721223e5879cb44d7186a2f32282371c7fb6090a.tar.gz | |
fix bug use target dataset for top n correlation
| -rw-r--r-- | wqflask/wqflask/correlation/rust_correlation.py | 21 |
1 files changed, 11 insertions, 10 deletions
diff --git a/wqflask/wqflask/correlation/rust_correlation.py b/wqflask/wqflask/correlation/rust_correlation.py index 88133b31..5c4d0b8a 100644 --- a/wqflask/wqflask/correlation/rust_correlation.py +++ b/wqflask/wqflask/correlation/rust_correlation.py @@ -99,7 +99,7 @@ def chunk_dataset(dataset, steps, name): for i in range(0, len(dataset), steps): matrix = list(dataset[i:i + steps]) results.append([traits_name_dict[matrix[0][0]]] + [str(value) - for (trait_name, strain, value) in matrix]) + for (trait_name, strain, value) in matrix]) return results @@ -159,9 +159,9 @@ def compute_top_n_sample(start_vars, dataset, trait_list): corr_data, list(sample_data.values()), "pearson", ",") -def compute_top_n_lit(corr_results, this_dataset, this_trait) -> dict: +def compute_top_n_lit(corr_results, target_dataset, this_trait) -> dict: (this_trait_geneid, geneid_dict, species) = do_lit_correlation( - this_trait, this_dataset) + this_trait, target_dataset) geneid_dict = {trait_name: geneid for (trait_name, geneid) in geneid_dict.items() if @@ -177,14 +177,14 @@ def compute_top_n_lit(corr_results, this_dataset, this_trait) -> dict: return {} -def compute_top_n_tissue(this_dataset, this_trait, traits, method): +def compute_top_n_tissue(target_dataset, this_trait, traits, method): # refactor lots of rpt trait_symbol_dict = dict({ trait_name: symbol for (trait_name, symbol) - in this_dataset.retrieve_genes("Symbol").items() + in target_dataset.retrieve_genes("Symbol").items() if traits.get(trait_name)}) corr_result_tissue_vals_dict = get_trait_symbol_and_tissue_values( @@ -248,7 +248,6 @@ def __compute_sample_corr__( target_dataset.get_trait_data(list(sample_data.keys())) - def __merge_key_and_values__(rows, current): wo_nones = [value for value in current[1] if value is not None] if len(wo_nones) > 0: @@ -265,12 +264,14 @@ def __compute_sample_corr__( target_data, list(sample_data.values()), method, ",", corr_type, n_top) + def __datasets_compatible_p__(trait_dataset, target_dataset, corr_method): return not ( corr_method in ("tissue", "Tissue r", "Literature r", "lit") and (trait_dataset.type == "ProbeSet" and target_dataset.type in ("Publish", "Geno"))) + def __compute_tissue_corr__( start_vars: dict, corr_type: str, method: str, n_top: int, target_trait_info: tuple): @@ -344,9 +345,9 @@ def compute_correlation_rust( if corr_type == "sample": top_a = compute_top_n_tissue( - this_dataset, this_trait, results, method) + target_dataset, this_trait, results, method) - top_b = compute_top_n_lit(results, this_dataset, this_trait) + top_b = compute_top_n_lit(results, target_dataset, this_trait) elif corr_type == "lit": @@ -355,14 +356,14 @@ def compute_correlation_rust( top_a = compute_top_n_sample( start_vars, target_dataset, list(results.keys())) top_b = compute_top_n_tissue( - this_dataset, this_trait, results, method) + target_dataset, this_trait, results, method) else: top_a = compute_top_n_sample( start_vars, target_dataset, list(results.keys())) - top_b = compute_top_n_lit(results, this_dataset, this_trait) + top_b = compute_top_n_lit(results, target_dataset, this_trait) return { "correlation_results": merge_results( |