"""module contains code for correlations""" import multiprocessing from typing import List from typing import Tuple from typing import Optional from typing import Callable import scipy.stats from gn3.computations.biweight import call_biweight_script def map_shared_keys_to_values(target_sample_keys: List, target_sample_vals: dict) -> List: """Function to construct target dataset data items given common shared keys and trait sample-list values for example given keys >>>>>>>>>> ["BXD1", "BXD2", "BXD5", "BXD6", "BXD8", "BXD9"] and value object as "HCMA:_AT": [4.1, 5.6, 3.2, 1.1, 4.4, 2.2],TXD_AT": [6.2, 5.7, 3.6, 1.5, 4.2, 2.3]} return results should be a list of dicts mapping the shared keys to the trait values """ target_dataset_data = [] for trait_id, sample_values in target_sample_vals.items(): target_trait_dict = dict(zip(target_sample_keys, sample_values)) target_trait = { "trait_id": trait_id, "trait_sample_data": target_trait_dict } target_dataset_data.append(target_trait) return target_dataset_data def normalize_values(a_values: List, b_values: List) -> Tuple[List[float], List[float], int]: """Trim two lists of values to contain only the values they both share Given two lists of sample values, trim each list so that it contains only the samples that contain a value in both lists. Also returns the number of such samples. >>> normalize_values([2.3, None, None, 3.2, 4.1, 5], [3.4, 7.2, 1.3, None, 6.2, 4.1]) ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3) """ a_new = [] b_new = [] for a_val, b_val in zip(a_values, b_values): if (a_val and b_val is not None): a_new.append(a_val) b_new.append(b_val) return a_new, b_new, len(a_new) def compute_corr_coeff_p_value(primary_values: List, target_values: List, corr_method: str) -> Tuple[float, float]: """Given array like inputs calculate the primary and target_value methods -> pearson,spearman and biweight mid correlation return value is rho and p_value """ corr_mapping = { "bicor": do_bicor, "pearson": scipy.stats.pearsonr, "spearman": scipy.stats.spearmanr } use_corr_method = corr_mapping.get(corr_method, "spearman") corr_coeffient, p_val = use_corr_method(primary_values, target_values) return (corr_coeffient, p_val) def compute_sample_r_correlation(trait_name, corr_method, trait_vals, target_samples_vals) -> Optional[ Tuple[str, float, float, int]]: """Given a primary trait values and target trait values calculate the correlation coeff and p value """ (sanitized_traits_vals, sanitized_target_vals, num_overlap) = normalize_values(trait_vals, target_samples_vals) if num_overlap > 5: (corr_coeffient, p_value) =\ compute_corr_coeff_p_value(primary_values=sanitized_traits_vals, target_values=sanitized_target_vals, corr_method=corr_method) if corr_coeffient is not None: return (trait_name, corr_coeffient, p_value, num_overlap) return None def do_bicor(x_val, y_val) -> Tuple[float, float]: """Not implemented method for doing biweight mid correlation use astropy stats package :not packaged in guix """ try: results = call_biweight_script(x_val, y_val) except Exception as error: raise error return results def filter_shared_sample_keys(this_samplelist, target_samplelist) -> Tuple[List, List]: """Given primary and target sample-list for two base and target trait select filter the values using the shared keys """ this_vals = [] target_vals = [] for key, value in target_samplelist.items(): if key in this_samplelist: target_vals.append(value) this_vals.append(this_samplelist[key]) return (this_vals, target_vals) def compute_all_sample_correlation(this_trait, target_dataset, corr_method="pearson") -> List: """Given a trait data sample-list and target__datasets compute all sample correlation """ # xtodo fix trait_name currently returning single one # pylint: disable-msg=too-many-locals this_trait_samples = this_trait["trait_sample_data"] corr_results = [] processed_values = [] for target_trait in target_dataset: trait_name = target_trait.get("trait_id") target_trait_data = target_trait["trait_sample_data"] processed_values.append((trait_name, corr_method, *filter_shared_sample_keys( this_trait_samples, target_trait_data))) with multiprocessing.Pool(4) as pool: results = pool.starmap(compute_sample_r_correlation, processed_values) for sample_correlation in results: if sample_correlation is not None: (trait_name, corr_coeffient, p_value, num_overlap) = sample_correlation corr_result = { "corr_coeffient": corr_coeffient, "p_value": p_value, "num_overlap": num_overlap } corr_results.append({trait_name: corr_result}) return sorted( corr_results, key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coeffient"])) def benchmark_compute_all_sample(this_trait, target_dataset, corr_method="pearson") -> List: """Temp function to benchmark with compute_all_sample_r alternative to compute_all_sample_r where we use multiprocessing """ this_trait_samples = this_trait["trait_sample_data"] corr_results = [] for target_trait in target_dataset: trait_name = target_trait.get("trait_id") target_trait_data = target_trait["trait_sample_data"] this_vals, target_vals = filter_shared_sample_keys( this_trait_samples, target_trait_data) sample_correlation = compute_sample_r_correlation( trait_name=trait_name, corr_method=corr_method, trait_vals=this_vals, target_samples_vals=target_vals) if sample_correlation is not None: (trait_name, corr_coeffient, p_value, num_overlap) = sample_correlation else: continue corr_result = { "corr_coeffient": corr_coeffient, "p_value": p_value, "num_overlap": num_overlap } corr_results.append({trait_name: corr_result}) return corr_results def tissue_correlation_for_trait( primary_tissue_vals: List, target_tissues_values: List, corr_method: str, trait_id: str, compute_corr_p_value: Callable = compute_corr_coeff_p_value) -> dict: """Given a primary tissue values for a trait and the target tissues values compute the correlation_cooeff and p value the input required are arrays output -> List containing Dicts with corr_coefficient value,P_value and also the tissue numbers is len(primary) == len(target) """ # ax :todo assertion that length one one target tissue ==primary_tissue (tissue_corr_coeffient, p_value) = compute_corr_p_value(primary_values=primary_tissue_vals, target_values=target_tissues_values, corr_method=corr_method) tiss_corr_result = {trait_id: { "tissue_corr": tissue_corr_coeffient, "tissue_number": len(primary_tissue_vals), "tissue_p_val": p_value}} return tiss_corr_result def fetch_lit_correlation_data( conn, input_mouse_gene_id: Optional[str], gene_id: str, mouse_gene_id: Optional[str] = None) -> Tuple[str, float]: """Given input trait mouse gene id and mouse gene id fetch the lit corr_data """ if mouse_gene_id is not None and ";" not in mouse_gene_id: query = """ SELECT VALUE FROM LCorrRamin3 WHERE GeneId1='%s' and GeneId2='%s' """ query_values = (str(mouse_gene_id), str(input_mouse_gene_id)) cursor = conn.cursor() cursor.execute(query_formatter(query, *query_values)) results = cursor.fetchone() lit_corr_results = None if results is not None: lit_corr_results = results else: cursor = conn.cursor() cursor.execute(query_formatter(query, *tuple(reversed(query_values)))) lit_corr_results = cursor.fetchone() lit_results = (gene_id, lit_corr_results[0])\ if lit_corr_results else (gene_id, 0) return lit_results return (gene_id, 0) def lit_correlation_for_trait( conn, target_trait_lists: List, species: Optional[str] = None, trait_gene_id: Optional[str] = None) -> List: """given species,base trait gene id fetch the lit corr results from the db\ output is float for lit corr results """ fetched_lit_corr_results = [] this_trait_mouse_gene_id = map_to_mouse_gene_id(conn=conn, species=species, gene_id=trait_gene_id) for (trait_name, target_trait_gene_id) in target_trait_lists: corr_results = {} if target_trait_gene_id: target_mouse_gene_id = map_to_mouse_gene_id( conn=conn, species=species, gene_id=target_trait_gene_id) fetched_corr_data = fetch_lit_correlation_data( conn=conn, input_mouse_gene_id=this_trait_mouse_gene_id, gene_id=target_trait_gene_id, mouse_gene_id=target_mouse_gene_id) dict_results = dict(zip(("gene_id", "lit_corr"), fetched_corr_data)) corr_results[trait_name] = dict_results fetched_lit_corr_results.append(corr_results) return fetched_lit_corr_results def query_formatter(query_string: str, *query_values): """Formatter query string given the unformatted query string and the respectibe values.Assumes number of placeholders is equal to the number of query values """ # xtodo escape sql queries return query_string % (query_values) def map_to_mouse_gene_id(conn, species: Optional[str], gene_id: Optional[str]) -> Optional[str]: """Given a species which is not mouse map the gene_id\ to respective mouse gene id""" if None in (species, gene_id): return None if species == "mouse": return gene_id cursor = conn.cursor() query = """SELECT mouse FROM GeneIDXRef WHERE '%s' = '%s'""" query_values = (species, gene_id) cursor.execute(query_formatter(query, *query_values)) results = cursor.fetchone() mouse_gene_id = results.mouse if results is not None else None return mouse_gene_id def compute_all_lit_correlation(conn, trait_lists: List, species: str, gene_id): """Function that acts as an abstraction for lit_correlation_for_trait""" lit_results = lit_correlation_for_trait( conn=conn, target_trait_lists=trait_lists, species=species, trait_gene_id=gene_id) sorted_lit_results = sorted( lit_results, key=lambda trait_name: -abs(list(trait_name.values())[0]["lit_corr"])) return sorted_lit_results def compute_all_tissue_correlation(primary_tissue_dict: dict, target_tissues_data: dict, corr_method: str): """Function acts as an abstraction for tissue_correlation_for_trait\ required input are target tissue object and primary tissue trait\ target tissues data contains the trait_symbol_dict and symbol_tissue_vals """ tissues_results = [] primary_tissue_vals = primary_tissue_dict["tissue_values"] traits_symbol_dict = target_tissues_data["trait_symbol_dict"] symbol_tissue_vals_dict = target_tissues_data["symbol_tissue_vals_dict"] target_tissues_list = process_trait_symbol_dict( traits_symbol_dict, symbol_tissue_vals_dict) for target_tissue_obj in target_tissues_list: trait_id = target_tissue_obj.get("trait_id") target_tissue_vals = target_tissue_obj.get("tissue_values") tissue_result = tissue_correlation_for_trait( primary_tissue_vals=primary_tissue_vals, target_tissues_values=target_tissue_vals, trait_id=trait_id, corr_method=corr_method) tissue_result_dict = {trait_id: tissue_result} tissues_results.append(tissue_result_dict) return sorted( tissues_results, key=lambda trait_name: -abs(list(trait_name.values())[0]["tissue_corr"])) def process_trait_symbol_dict(trait_symbol_dict, symbol_tissue_vals_dict) -> List: """Method for processing trait symbol dict given the symbol tissue values """ traits_tissue_vals = [] for (trait, symbol) in trait_symbol_dict.items(): if symbol is not None: target_symbol = symbol.lower() if target_symbol in symbol_tissue_vals_dict: trait_tissue_val = symbol_tissue_vals_dict[target_symbol] target_tissue_dict = {"trait_id": trait, "symbol": target_symbol, "tissue_values": trait_tissue_val} traits_tissue_vals.append(target_tissue_dict) return traits_tissue_vals def compute_tissue_correlation(primary_tissue_dict: dict, target_tissues_data: dict, corr_method: str): """Experimental function that uses multiprocessing for computing tissue correlation """ tissues_results = [] primary_tissue_vals = primary_tissue_dict["tissue_values"] traits_symbol_dict = target_tissues_data["trait_symbol_dict"] symbol_tissue_vals_dict = target_tissues_data["symbol_tissue_vals_dict"] target_tissues_list = process_trait_symbol_dict( traits_symbol_dict, symbol_tissue_vals_dict) processed_values = [] for target_tissue_obj in target_tissues_list: trait_id = target_tissue_obj.get("trait_id") target_tissue_vals = target_tissue_obj.get("tissue_values") processed_values.append( (primary_tissue_vals, target_tissue_vals, corr_method, trait_id)) with multiprocessing.Pool(4) as pool: results = pool.starmap( tissue_correlation_for_trait, processed_values) for result in results: tissues_results.append(result) return sorted( tissues_results, key=lambda trait_name: -abs(list(trait_name.values())[0]["tissue_corr"]))