diff options
Diffstat (limited to 'gn3/computations')
| -rw-r--r-- | gn3/computations/partial_correlations.py | 131 |
1 files changed, 70 insertions, 61 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py index 869bee4..231b0a7 100644 --- a/gn3/computations/partial_correlations.py +++ b/gn3/computations/partial_correlations.py @@ -14,12 +14,20 @@ import pingouin from scipy.stats import pearsonr, spearmanr from gn3.settings import TEXTDIR +from gn3.random import random_string from gn3.function_helpers import compose from gn3.data_helpers import parse_csv_line from gn3.db.traits import export_informative from gn3.db.traits import retrieve_trait_info, retrieve_trait_data from gn3.db.species import species_name, translate_to_mouse_gene_id -from gn3.db.correlations import get_filename, fetch_all_database_data +from gn3.db.correlations import ( + get_filename, + fetch_all_database_data, + check_for_literature_info, + fetch_tissue_correlations, + fetch_literature_correlations, + check_symbol_for_tissue_correlation, + fetch_gene_symbol_tissue_value_dict_for_trait) def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]): """ @@ -311,7 +319,7 @@ def compute_partial( zero_order_corr = pingouin.corr( datafrm["x"], datafrm["y"], method=( - "pearson" if "pearson" in method.lower() else "spearman")) + "pearson" if "pearson" in method.lower() else "spearman")) if math.isnan(pc_coeff): return ( @@ -371,9 +379,10 @@ def partial_correlations_normal(# pylint: disable=R0913 return len(trait_database), all_correlations -def partial_corrs( - conn, samples , primary_vals, control_vals, return_number, species, input_trait_geneid, - input_trait_symbol, tissue_probeset_freeze_id, method, dataset, database_filename): +def partial_corrs(# pylint: disable=[R0913] + conn, samples, primary_vals, control_vals, return_number, species, + input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id, + method, dataset, database_filename): """ Compute the partial correlations, selecting the fast or normal method depending on the existence of the database text file. @@ -404,8 +413,7 @@ def partial_corrs( data_start_pos, dataset, method) def literature_correlation_by_list( - conn: Any, input_trait_mouse_geneid: int, species: str, - trait_list: Tuple[dict]) -> Tuple[dict]: + conn: Any, species: str, trait_list: Tuple[dict]) -> Tuple[dict]: """ This is a migration of the `web.webqtl.correlation.CorrelationPage.getLiteratureCorrelationByList` @@ -415,16 +423,16 @@ def literature_correlation_by_list( bool(t.get("tissue_corr")) and bool(t.get("tissue_p_value"))))(trait) for trait in trait_list): - temp_table_name = f"LITERATURE{random_string(8)}" - q1 = ( + temporary_table_name = f"LITERATURE{random_string(8)}" + query1 = ( f"CREATE TEMPORARY TABLE {temporary_table_name} " "(GeneId1 INT(12) UNSIGNED, GeneId2 INT(12) UNSIGNED PRIMARY KEY, " "value DOUBLE)") - q2 = ( + query2 = ( f"INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) " "SELECT GeneId1, GeneId2, value FROM LCorrRamin3 " "WHERE GeneId1=%(geneid)s") - q3 = ( + query3 = ( "INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) " "SELECT GeneId2, GeneId1, value FROM LCorrRamin3 " "WHERE GeneId2=%s AND GeneId1 != %(geneid)s") @@ -433,7 +441,8 @@ def literature_correlation_by_list( if trait.get("geneid"): return { **trait, - "mouse_geneid": translate_to_mouse_gene_id(trait.get("geneid")) + "mouse_geneid": translate_to_mouse_gene_id( + species, trait.get("geneid"), conn) } return {**trait, "mouse_geneid": 0} @@ -441,13 +450,13 @@ def literature_correlation_by_list( cursor.execute( f"SELECT GeneId2, value FROM {temporary_table_name} " "WHERE GeneId2 IN %(geneids)s", - geneids = geneids) - return {geneid: value for geneid, value in cursor.fetchall()} + geneids=geneids) + return dict(cursor.fetchall()) with conn.cursor() as cursor: - cursor.execute(q1) - cursor.execute(q2) - cursor.execute(q3) + cursor.execute(query1) + cursor.execute(query2) + cursor.execute(query3) traits = tuple(__set_mouse_geneid__(trait) for trait in trait_list) lcorrs = __retrieve_lcorr__( @@ -470,9 +479,9 @@ def tissue_correlation_by_list( `web.webqtl.correlation.CorrelationPage.getTissueCorrelationByList` function in GeneNetwork1. """ - def __add_tissue_corr__(trait, primary_trait_value, trait_value): + def __add_tissue_corr__(trait, primary_trait_values, trait_values): result = pingouin.corr( - primary_trait_values, target_trait_values, + primary_trait_values, trait_values, method=("spearman" if "spearman" in method.lower() else "pearson")) return { **trait, @@ -484,7 +493,8 @@ def tissue_correlation_by_list( prim_trait_symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait( (primary_trait_symbol,), tissue_probeset_freeze_id, conn) if primary_trait_symbol.lower() in prim_trait_symbol_value_dict: - primary_trait_value = prim_trait_symbol_value_dict[prim_trait_symbol.lower()] + primary_trait_value = prim_trait_symbol_value_dict[ + primary_trait_symbol.lower()] gene_symbol_list = tuple( trait for trait in trait_list if "symbol" in trait.keys()) symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait( @@ -504,7 +514,7 @@ def tissue_correlation_by_list( } for trait in trait_list) return trait_list -def partial_correlations_entry( +def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911] conn: Any, primary_trait_name: str, control_trait_names: Tuple[str, ...], method: str, criteria: int, group: str, target_db_name: str) -> dict: @@ -524,7 +534,7 @@ def partial_correlations_entry( primary_trait = retrieve_trait_info(threshold, primary_trait_name, conn) primary_trait_data = retrieve_trait_data(primary_trait, conn) - primary_samples, primary_values, primary_variances = export_informative( + primary_samples, primary_values, _primary_variances = export_informative( primary_trait_data) cntrl_traits = tuple( @@ -537,8 +547,8 @@ def partial_correlations_entry( (cntrl_samples, cntrl_values, - cntrl_variances, - cntrl_ns) = control_samples(cntrl_traits_data, primary_samples) + _cntrl_variances, + _cntrl_ns) = control_samples(cntrl_traits_data, primary_samples) common_primary_control_samples = primary_samples fixed_primary_vals = primary_values @@ -547,8 +557,8 @@ def partial_correlations_entry( (common_primary_control_samples, fixed_primary_vals, fixed_control_vals, - primary_variances, - cntrl_variances) = fix_samples(primary_trait, cntrl_traits) + _primary_variances, + _cntrl_variances) = fix_samples(primary_trait, cntrl_traits) if len(common_primary_control_samples) < corr_min_informative: return { @@ -580,7 +590,6 @@ def partial_correlations_entry( tissue_probeset_freeze_id = 1 db_type = primary_trait["db"]["dataset_type"] - db_name = primary_trait["db"]["dataset_name"] if db_type == "ProbeSet" and method.lower() in ( "sgo literature correlation", @@ -605,10 +614,11 @@ def partial_correlations_entry( "associated Literature Information."), "error_type": "Literature Correlation"} - if (method.lower() in ( - "tissue correlation, pearson's r", - "tissue correlation, spearman's rho") - and input_trait_symbol is None): + if ( + method.lower() in ( + "tissue correlation, pearson's r", + "tissue correlation, spearman's rho") + and input_trait_symbol is None): return { "status": "error", "message": ( @@ -616,11 +626,12 @@ def partial_correlations_entry( "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)): + 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": ( @@ -629,7 +640,7 @@ def partial_correlations_entry( "error_type": "Tissue Correlation"} database_filename = get_filename(conn, target_db_name, TEXTDIR) - total_traits, all_correlations = partial_corrs( + _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, @@ -637,11 +648,11 @@ def partial_correlations_entry( def __make_sorter__(method): - def __sort_6__(x): - return x[6] + def __sort_6__(row): + return row[6] - def __sort_3__(x): - return x[3] + def __sort_3__(row): + return row[3] if "literature" in method.lower(): return __sort_6__ @@ -655,33 +666,31 @@ def partial_correlations_entry( all_correlations, key=__make_sorter__(method)) add_lit_corr_and_tiss_corr = compose( - partial( - literature_correlation_by_list, conn, input_trait_mouse_geneid, - species), + partial(literature_correlation_by_list, conn, species), partial( tissue_correlation_by_list, conn, input_trait_symbol, tissue_probeset_freeze_id, method)) trait_list = add_lit_corr_and_tiss_corr(tuple( - { - **retrieve_trait_info( - threshold, - f"{primary_trait['db']['dataset_name']}::{item[0]}", - conn), - "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]} + { + **retrieve_trait_info( + threshold, + f"{primary_trait['db']['dataset_name']}::{item[0]}", + conn), + "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]} + **({"l_corr": item[6]} if len(item) == 7 else {}) - } + } for item in - sorted_correlations[:min(criteria, len(all_correlations))])) + sorted_correlations[:min(criteria, len(all_correlations))])) return trait_list |