diff options
| -rw-r--r-- | gn3/computations/partial_correlations.py | 60 |
1 files changed, 44 insertions, 16 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py index 4f45159..4bd26a2 100644 --- a/gn3/computations/partial_correlations.py +++ b/gn3/computations/partial_correlations.py @@ -200,22 +200,6 @@ def good_dataset_samples_indexes( samples_from_file.index(good) for good in set(samples).intersection(set(samples_from_file)))) -def determine_partials( - primary_vals, control_vals, all_target_trait_names, - all_target_trait_values, method): - """ - This **WILL** be a migration of - `web.webqtl.correlation.correlationFunction.determinePartialsByR` function - in GeneNetwork1. - - The function in GeneNetwork1 contains code written in R that is then used to - compute the partial correlations. - """ - ## This function is not implemented at this stage - return tuple( - primary_vals, control_vals, all_target_trait_names, - all_target_trait_values, method) - def compute_partial_correlations_fast(# pylint: disable=[R0913, R0914] samples, primary_vals, control_vals, database_filename, fetched_correlations, method: str, correlation_type: str) -> Tuple[ @@ -330,3 +314,47 @@ def compute_partial( return tuple( __compute_trait_info__(target) for target in zip(target_vals, target_names)) + +def partial_correlations_normal(# pylint: disable=R0913 + primary_vals, control_vals, input_trait_gene_id, trait_database, + data_start_pos: int, db_type: str, method: str) -> Tuple[ + float, Tuple[float, ...]]: + """ + Computes the correlation coefficients. + + This is a migration of the + `web.webqtl.correlation.PartialCorrDBPage.getPartialCorrelationsNormal` + function in GeneNetwork1. + """ + def __add_lit_and_tiss_corr__(item): + if method.lower() == "sgo literature correlation": + # if method is 'SGO Literature Correlation', `compute_partial` + # would give us LitCorr in the [1] position + return tuple(item) + trait_database[1] + if method.lower() in ( + "tissue correlation, pearson's r", + "tissue correlation, spearman's rho"): + # if method is 'Tissue Correlation, *', `compute_partial` would give + # us Tissue Corr in the [1] position and Tissue Corr P Value in the + # [2] position + return tuple(item) + (trait_database[1], trait_database[2]) + return item + + target_trait_names, target_trait_vals = reduce( + lambda acc, item: (acc[0]+(item[0],), acc[1]+(item[data_start_pos:],)), + trait_database, (tuple(), tuple())) + + all_correlations = compute_partial( + primary_vals, control_vals, target_trait_vals, target_trait_names, + method) + + if (input_trait_gene_id and db_type == "ProbeSet" and method.lower() in ( + "sgo literature correlation", "tissue correlation, pearson's r", + "tissue correlation, spearman's rho")): + return ( + len(trait_database), + tuple( + __add_lit_and_tiss_corr__(item) + for idx, item in enumerate(all_correlations))) + + return len(trait_database), all_correlations |