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| author | Alexander Kabui | 2021-05-15 00:57:19 +0300 |
|---|---|---|
| committer | BonfaceKilz | 2021-05-17 10:02:01 +0300 |
| commit | 2ddc4c1d3216e400a4bed191b1a2a469512c2535 (patch) | |
| tree | 8849440992f30e5067b675ff8b4b5855643af509 | |
| parent | d24b1d542f0fc84a3a2c214516459d0a3a9f97a4 (diff) | |
| download | genenetwork2-2ddc4c1d3216e400a4bed191b1a2a469512c2535.tar.gz | |
handle None for tissue input data
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_gn3_api.py | 31 |
1 files changed, 20 insertions, 11 deletions
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py index 46202ca3..6974dbd5 100644 --- a/wqflask/wqflask/correlation/correlation_gn3_api.py +++ b/wqflask/wqflask/correlation/correlation_gn3_api.py @@ -80,13 +80,16 @@ def tissue_for_trait_lists(corr_results, this_dataset, this_trait): traits_symbol_dict = this_dataset.retrieve_genes("Symbol") traits_symbol_dict = dict({trait_name: symbol for ( trait_name, symbol) in traits_symbol_dict.items() if trait_lists.get(trait_name)}) - primary_tissue_data, target_tissue_data = get_tissue_correlation_input( + tissue_input = get_tissue_correlation_input( this_trait, traits_symbol_dict) - corr_results = compute_tissue_correlation( - primary_tissue_dict=primary_tissue_data, - target_tissues_data=target_tissue_data, - corr_method="pearson") - return corr_results + + if tissue_input is not None: + (primary_tissue_data, target_tissue_data) = tissue_input + corr_results = compute_tissue_correlation( + primary_tissue_dict=primary_tissue_data, + target_tissues_data=target_tissue_data, + corr_method="pearson") + return corr_results def lit_for_trait_list(corr_results, this_dataset, this_trait): @@ -153,9 +156,12 @@ def compute_correlation(start_vars, method="pearson"): elif corr_type == "tissue": trait_symbol_dict = this_dataset.retrieve_genes("Symbol") - primary_tissue_data, target_tissue_data = get_tissue_correlation_input( + tissue_input = get_tissue_correlation_input( this_trait, trait_symbol_dict) + if tissue_input is not None: + (primary_tissue_data, target_tissue_data) = tissue_input + corr_input_data = { "primary_tissue": primary_tissue_data, "target_tissues_dict": target_tissue_data @@ -208,15 +214,18 @@ def compute_corr_for_top_results(correlation_results, tissue_result = tissue_for_trait_lists( correlation_results, this_dataset, this_trait) - correlation_results = merge_correlation_results( - correlation_results, tissue_result) + if tissue_result: + + correlation_results = merge_correlation_results( + correlation_results, tissue_result) if corr_type != "lit" and this_dataset.type == "ProbeSet" and target_dataset.type == "ProbeSet": lit_result = lit_for_trait_list( correlation_results, this_dataset, this_trait) - correlation_results = merge_correlation_results( - correlation_results, lit_result) + if lit_result: + correlation_results = merge_correlation_results( + correlation_results, lit_result) if corr_type != "sample": pass |