diff options
| -rw-r--r-- | gn3/computations/partial_correlations.py | 74 | ||||
| -rwxr-xr-x | scripts/partial_correlations.py | 4 | ||||
| -rw-r--r-- | tests/integration/test_partial_correlations.py | 4 |
3 files changed, 57 insertions, 25 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py index 2921852..f82031a 100644 --- a/gn3/computations/partial_correlations.py +++ b/gn3/computations/partial_correlations.py @@ -555,26 +555,14 @@ def trait_for_output(trait): } return {key: val for key, val in trait.items() if val is not None} -def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] - conn: Any, primary_trait_name: str, - control_trait_names: Tuple[str, ...], method: str, - criteria: int, target_db_name: str) -> dict: - """ - This is the 'ochestration' function for the partial-correlation feature. - - This function will dispatch the functions doing data fetches from the - database (and various other places) and feed that data to the functions - doing the conversions and computations. It will then return the results of - all of that work. - - This function is doing way too much. Look into splitting out the - functionality into smaller functions that do fewer things. - """ +def check_for_common_errors(conn, primary_trait_name, control_trait_names): + """Check for common errors""" threshold = 0 corr_min_informative = 4 + non_error_result = {"status": "success"} - all_traits = traits_info( - conn, threshold, (primary_trait_name,) + control_trait_names) + all_traits = tuple(item for item in traits_info( + conn, threshold, (primary_trait_name,) + control_trait_names)) all_traits_data = traits_data(conn, all_traits) primary_trait = tuple( @@ -585,6 +573,8 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] "status": "not-found", "message": f"Could not find primary trait {primary_trait['trait_fullname']}" } + non_error_result["primary_trait"] = primary_trait + cntrl_traits = tuple( trait for trait in all_traits if trait["trait_fullname"] != primary_trait_name) @@ -599,15 +589,25 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] "- continuing without it."), category=UserWarning) - group = primary_trait["db"]["group"] + non_error_result["control_traits"] = cntrl_traits + + non_error_result["group"] = group = primary_trait["db"]["group"] primary_trait_data = all_traits_data[primary_trait["trait_name"]] + non_error_result[""] = primary_trait_data + primary_samples, primary_values, _primary_variances = export_informative( primary_trait_data) + non_error_result["primary_samples"] = primary_samples + non_error_result["primary_values"] = primary_values + non_error_result["primary_variances"] = _primary_variances cntrl_traits_data = tuple( data for trait_name, data in all_traits_data.items() if trait_name != primary_trait["trait_name"]) + non_error_result["control_traits_data"] = cntrl_traits_data + species = species_name(conn, group) + non_error_result["species"] = species (cntrl_samples, cntrl_values, @@ -632,6 +632,11 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] f"{group} dataset. No calculation of correlation has been " "attempted."), "error_type": "Inadequate Samples"} + non_error_result["common_primary_control_samples"] = common_primary_control_samples + non_error_result["fixed_primary_values"] = fixed_primary_vals + non_error_result["fixed_control_values"] = fixed_control_vals + non_error_result["primary_variances"] = _primary_variances + non_error_result["control_variances"] = _cntrl_variances identical_traits_names = find_identical_traits( primary_trait_name, primary_values, control_trait_names, cntrl_values) @@ -646,7 +651,32 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] "partial correlation cannot be computed. Please re-select your " "traits."), "error_type": "Identical Traits"} + non_error_result["identical_traits_names"] = identical_traits_names + + return non_error_result + +def partial_correlations_with_target_db(# pylint: disable=[R0913, R0914, R0911] + conn: Any, primary_trait_name: str, + control_trait_names: Tuple[str, ...], method: str, + criteria: int, target_db_name: str) -> dict: + """ + This is the 'ochestration' function for the partial-correlation feature. + + This function will dispatch the functions doing data fetches from the + database (and various other places) and feed that data to the functions + doing the conversions and computations. It will then return the results of + all of that work. + + This function is doing way too much. Look into splitting out the + functionality into smaller functions that do fewer things. + """ + + check_res = check_for_common_errors( + conn, primary_trait_name, control_trait_names) + if check_res.get("status") == "error": + return error_check_results + primary_trait = check_res["primary_trait"] input_trait_geneid = primary_trait.get("geneid", 0) input_trait_symbol = primary_trait.get("symbol", "") input_trait_mouse_geneid = translate_to_mouse_gene_id( @@ -713,10 +743,12 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] database_filename = get_filename(conn, target_db_name, TEXTDIR) _total_traits, all_correlations = partial_corrs( - conn, common_primary_control_samples, fixed_primary_vals, - fixed_control_vals, len(fixed_primary_vals), species, + conn, check_res["common_primary_control_samples"], + check_res["fixed_primary_values"], check_res["fixed_control_vals"], + len(check_res["fixed_primary_vals"]), check_res["species"], input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id, - method, {**target_dataset, "dataset_type": target_dataset["type"]}, database_filename) + method, {**target_dataset, "dataset_type": target_dataset["type"]}, + database_filename) def __make_sorter__(method): diff --git a/scripts/partial_correlations.py b/scripts/partial_correlations.py index f203daa..52bde4c 100755 --- a/scripts/partial_correlations.py +++ b/scripts/partial_correlations.py @@ -5,7 +5,7 @@ from argparse import ArgumentParser from gn3.db_utils import database_connector from gn3.responses.pcorrs_responses import OutputEncoder -from gn3.computations.partial_correlations import partial_correlations_entry +from gn3.computations.partial_correlations import partial_correlations_with_target_db def process_cli_arguments(): parser = ArgumentParser() @@ -37,7 +37,7 @@ def cleanup_string(the_str): def run_partial_corrs(args): with database_connector() as conn: try: - return partial_correlations_entry( + return partial_correlations_with_target_db( conn, cleanup_string(args.primary_trait), tuple(cleanup_string(args.control_traits).split(",")), cleanup_string(args.method), args.criteria, diff --git a/tests/integration/test_partial_correlations.py b/tests/integration/test_partial_correlations.py index d249b42..fc9f64f 100644 --- a/tests/integration/test_partial_correlations.py +++ b/tests/integration/test_partial_correlations.py @@ -3,7 +3,7 @@ from unittest import mock import pytest -from gn3.computations.partial_correlations import partial_correlations_entry +from gn3.computations.partial_correlations import partial_correlations_with_target_db @pytest.mark.integration_test @pytest.mark.parametrize( @@ -220,5 +220,5 @@ def test_part_corr_api_with_mix_of_existing_and_non_existing_control_traits( """ criteria = 10 with pytest.warns(UserWarning): - partial_correlations_entry( + partial_correlations_with_target_db( db_conn, primary, controls, method, criteria, target) |