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authorFrederick Muriuki Muriithi2022-03-25 08:45:47 +0300
committerFrederick Muriuki Muriithi2022-03-29 04:17:36 +0300
commit5b0259c1d9c8735341e2ee19006c48aea44d7988 (patch)
treef79a2f1affdaa87d64ef4843bba661899361efbf /gn3/computations
parentb93b22386056347d8002dd2e403425beeb4657cd (diff)
downloadgenenetwork3-5b0259c1d9c8735341e2ee19006c48aea44d7988.tar.gz
Remove unused module
* Remove a module that is no longer in use
Diffstat (limited to 'gn3/computations')
-rw-r--r--gn3/computations/partial_correlations_optimised.py244
1 files changed, 0 insertions, 244 deletions
diff --git a/gn3/computations/partial_correlations_optimised.py b/gn3/computations/partial_correlations_optimised.py
deleted file mode 100644
index 601289c..0000000
--- a/gn3/computations/partial_correlations_optimised.py
+++ /dev/null
@@ -1,244 +0,0 @@
-"""
-This contains an optimised version of the
- `gn3.computations.partial_correlations.partial_correlations_entry`
-function.
-"""
-from functools import partial
-from typing import Any, Tuple
-
-from gn3.settings import TEXTDIR
-from gn3.function_helpers import compose
-from gn3.db.partial_correlations import traits_info, traits_data
-from gn3.db.species import species_name, translate_to_mouse_gene_id
-from gn3.db.traits import export_informative, retrieve_trait_dataset
-from gn3.db.correlations import (
- get_filename,
- check_for_literature_info,
- check_symbol_for_tissue_correlation)
-from gn3.computations.partial_correlations import (
- fix_samples,
- partial_corrs,
- control_samples,
- trait_for_output,
- find_identical_traits,
- tissue_correlation_by_list,
- literature_correlation_by_list)
-
-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.
- """
- threshold = 0
- corr_min_informative = 4
-
- all_traits = traits_info(
- conn, threshold, (primary_trait_name,) + control_trait_names)
- all_traits_data = traits_data(conn, all_traits)
-
- # primary_trait = retrieve_trait_info(threshold, primary_trait_name, conn)
- primary_trait = tuple(
- trait for trait in all_traits
- if trait["trait_fullname"] == primary_trait_name)[0]
- group = primary_trait["db"]["group"]
- # primary_trait_data = retrieve_trait_data(primary_trait, conn)
- primary_trait_data = all_traits_data[primary_trait["trait_name"]]
- primary_samples, primary_values, _primary_variances = export_informative(
- primary_trait_data)
-
- # cntrl_traits = tuple(
- # retrieve_trait_info(threshold, trait_full_name, conn)
- # for trait_full_name in control_trait_names)
- # cntrl_traits_data = tuple(
- # retrieve_trait_data(cntrl_trait, conn)
- # for cntrl_trait in cntrl_traits)
- cntrl_traits = tuple(
- trait for trait in all_traits
- if trait["trait_fullname"] != primary_trait_name)
- cntrl_traits_data = tuple(
- data for trait_name, data in all_traits_data.items()
- if trait_name != primary_trait["trait_name"])
- species = species_name(conn, group)
-
- (cntrl_samples,
- cntrl_values,
- _cntrl_variances,
- _cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
-
- common_primary_control_samples = primary_samples
- fixed_primary_vals = primary_values
- fixed_control_vals = cntrl_values
- if not all(cnt_smp == primary_samples for cnt_smp in cntrl_samples):
- (common_primary_control_samples,
- fixed_primary_vals,
- fixed_control_vals,
- _primary_variances,
- _cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
-
- if len(common_primary_control_samples) < corr_min_informative:
- return {
- "status": "error",
- "message": (
- f"Fewer than {corr_min_informative} samples data entered for "
- f"{group} dataset. No calculation of correlation has been "
- "attempted."),
- "error_type": "Inadequate Samples"}
-
- identical_traits_names = find_identical_traits(
- primary_trait_name, primary_values, control_trait_names, cntrl_values)
- if len(identical_traits_names) > 0:
- return {
- "status": "error",
- "message": (
- f"{identical_traits_names[0]} and {identical_traits_names[1]} "
- "have the same values for the {len(fixed_primary_vals)} "
- "samples that will be used to compute the partial correlation "
- "(common for all primary and control traits). In such cases, "
- "partial correlation cannot be computed. Please re-select your "
- "traits."),
- "error_type": "Identical Traits"}
-
- input_trait_geneid = primary_trait.get("geneid", 0)
- input_trait_symbol = primary_trait.get("symbol", "")
- input_trait_mouse_geneid = translate_to_mouse_gene_id(
- species, input_trait_geneid, conn)
-
- tissue_probeset_freeze_id = 1
- db_type = primary_trait["db"]["dataset_type"]
-
- if db_type == "ProbeSet" and method.lower() in (
- "sgo literature correlation",
- "tissue correlation, pearson's r",
- "tissue correlation, spearman's rho"):
- return {
- "status": "error",
- "message": (
- "Wrong correlation type: It is not possible to compute the "
- f"{method} between your trait and data in the {target_db_name} "
- "database. Please try again after selecting another type of "
- "correlation."),
- "error_type": "Correlation Type"}
-
- if (method.lower() == "sgo literature correlation" and (
- bool(input_trait_geneid) is False or
- check_for_literature_info(conn, input_trait_mouse_geneid))):
- return {
- "status": "error",
- "message": (
- "No Literature Information: This gene does not have any "
- "associated Literature Information."),
- "error_type": "Literature Correlation"}
-
- if (
- method.lower() in (
- "tissue correlation, pearson's r",
- "tissue correlation, spearman's rho")
- and bool(input_trait_symbol) is False):
- return {
- "status": "error",
- "message": (
- "No Tissue Correlation Information: This gene does not have "
- "any associated Tissue Correlation Information."),
- "error_type": "Tissue Correlation"}
-
- if (
- method.lower() in (
- "tissue correlation, pearson's r",
- "tissue correlation, spearman's rho")
- and check_symbol_for_tissue_correlation(
- conn, tissue_probeset_freeze_id, input_trait_symbol)):
- return {
- "status": "error",
- "message": (
- "No Tissue Correlation Information: This gene does not have "
- "any associated Tissue Correlation Information."),
- "error_type": "Tissue Correlation"}
-
- target_dataset = retrieve_trait_dataset(
- ("Temp" if "Temp" in target_db_name else
- ("Publish" if "Publish" in target_db_name else
- "Geno" if "Geno" in target_db_name else "ProbeSet")),
- {"db": {"dataset_name": target_db_name}, "trait_name": "_"},
- threshold,
- conn)
-
- 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,
- input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id,
- method, {**target_dataset, "dataset_type": target_dataset["type"]}, database_filename)
-
-
- def __make_sorter__(method):
- def __sort_6__(row):
- return row[6]
-
- def __sort_3__(row):
- return row[3]
-
- if "literature" in method.lower():
- return __sort_6__
-
- if "tissue" in method.lower():
- return __sort_6__
-
- return __sort_3__
-
- # sorted_correlations = sorted(
- # all_correlations, key=__make_sorter__(method))
-
- add_lit_corr_and_tiss_corr = compose(
- partial(literature_correlation_by_list, conn, species),
- partial(
- tissue_correlation_by_list, conn, input_trait_symbol,
- tissue_probeset_freeze_id, method))
-
- selected_results = sorted(
- all_correlations,
- key=__make_sorter__(method))[:min(criteria, len(all_correlations))]
- traits_list_corr_info = {
- "{target_dataset['dataset_name']}::{item[0]}": {
- "noverlap": item[1],
- "partial_corr": item[2],
- "partial_corr_p_value": item[3],
- "corr": item[4],
- "corr_p_value": item[5],
- "rank_order": (1 if "spearman" in method.lower() else 0),
- **({
- "tissue_corr": item[6],
- "tissue_p_value": item[7]}
- if len(item) == 8 else {}),
- **({"l_corr": item[6]}
- if len(item) == 7 else {})
- } for item in selected_results}
-
- trait_list = add_lit_corr_and_tiss_corr(tuple(
- {**trait, **traits_list_corr_info.get(trait["trait_fullname"], {})}
- for trait in traits_info(
- conn, threshold,
- tuple(
- f"{target_dataset['dataset_name']}::{item[0]}"
- for item in selected_results))))
-
- return {
- "status": "success",
- "results": {
- "primary_trait": trait_for_output(primary_trait),
- "control_traits": tuple(
- trait_for_output(trait) for trait in cntrl_traits),
- "correlations": tuple(
- trait_for_output(trait) for trait in trait_list),
- "dataset_type": target_dataset["type"],
- "method": "spearman" if "spearman" in method.lower() else "pearson"
- }}