diff options
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/computations/correlations.py | 217 |
1 files changed, 124 insertions, 93 deletions
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py index 21f5929..d1d6ddb 100644 --- a/gn3/computations/correlations.py +++ b/gn3/computations/correlations.py @@ -7,20 +7,21 @@ from typing import Callable import scipy.stats # type: ignore -def compute_sum(rhs: int, lhs: int)-> int: - """initial tests to compute sum of two numbers""" +def compute_sum(rhs: int, lhs: int) -> int: + """Initial tests to compute sum of two numbers""" return rhs + lhs -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 +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. + 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]) + >>> 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) """ @@ -33,11 +34,11 @@ def normalize_values(a_values: List, b_values: List)->Tuple[List[float], List[fl 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 +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, @@ -52,13 +53,16 @@ def compute_corr_coeff_p_value(primary_values: List, target_values: List, corr_m return (corr_coeffient, p_val) -def compute_sample_r_correlation(corr_method: str, trait_vals, target_samples_vals)->\ - Optional[Tuple[float, float, int]]: - """Given a primary trait values and target trait values - calculate the correlation coeff and p value""" +def compute_sample_r_correlation( + corr_method: str, trait_vals, + target_samples_vals) -> Optional[Tuple[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) + """ + + (sanitized_traits_vals, sanitized_target_vals, + num_overlap) = normalize_values(trait_vals, target_samples_vals) if num_overlap > 5: @@ -67,7 +71,8 @@ def compute_sample_r_correlation(corr_method: str, trait_vals, target_samples_va target_values=sanitized_target_vals, corr_method=corr_method) - # xtodo check if corr_coefficient is None should use numpy.isNan scipy.isNan is deprecated + # xtodo check if corr_coefficient is None + # should use numpy.isNan scipy.isNan is deprecated if corr_coeffient is not None: return (corr_coeffient, p_value, num_overlap) @@ -75,29 +80,33 @@ def compute_sample_r_correlation(corr_method: str, trait_vals, target_samples_va 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 - """ + """Not implemented method for doing biweight mid correlation use astropy stats +package :not packaged in guix + """ return (x_val, y_val) -def filter_shared_sample_keys(this_samplelist, target_samplelist)->Tuple[List, List]: - """given primary and target samplelist for two base and target\ - trait select filter the values using the shared keys""" +def filter_shared_sample_keys(this_samplelist, + target_samplelist) -> Tuple[List, List]: + """Given primary and target samplelist 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 samplelist and target__datasets compute all sample correlation""" +def compute_all_sample_correlation(this_trait, + target_dataset, + corr_method="pearson") -> List: + """Given a trait data samplelist and target__datasets compute all sample +correlation""" this_trait_samples = this_trait["trait_sample_data"] @@ -110,7 +119,9 @@ def compute_all_sample_correlation(this_trait, target_dataset, corr_method="pear this_trait_samples, target_trait_data) sample_correlation = compute_sample_r_correlation( - corr_method=corr_method, trait_vals=this_vals, target_samples_vals=target_vals) + corr_method=corr_method, + trait_vals=this_vals, + target_samples_vals=target_vals) if sample_correlation is not None: (corr_coeffient, p_value, num_overlap) = sample_correlation @@ -118,9 +129,11 @@ def compute_all_sample_correlation(this_trait, target_dataset, corr_method="pear else: continue - corr_result = {"corr_coeffient": corr_coeffient, - "p_value": p_value, - "num_overlap": num_overlap} + corr_result = { + "corr_coeffient": corr_coeffient, + "p_value": p_value, + "num_overlap": num_overlap + } corr_results.append({trait_id: corr_result}) @@ -128,9 +141,11 @@ def compute_all_sample_correlation(this_trait, target_dataset, corr_method="pear def tissue_lit_corr_for_probe_type(corr_type: str, top_corr_results): - """function that does either lit_corr_for_trait_list or tissue_corr\ - _for_trait list depending on whether both dataset and target_dataset are\ - both set to probet""" + """Function that does either lit_corr_for_trait_list or tissue_corr _for_trait +list depending on whether both dataset and target_dataset are both set to +probet + + """ corr_results = {"lit": 1} @@ -148,22 +163,24 @@ def tissue_lit_corr_for_probe_type(corr_type: str, top_corr_results): return corr_results -def tissue_correlation_for_trait_list(primary_tissue_vals: List, - target_tissues_values: List, - corr_method: 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)""" +def tissue_correlation_for_trait_list( + primary_tissue_vals: List, + target_tissues_values: List, + corr_method: 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 lenggth 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) + (tissue_corr_coeffient, + p_value) = compute_corr_p_value(primary_values=primary_tissue_vals, + target_values=target_tissues_values, + corr_method=corr_method) lit_corr_result = { "tissue_corr": tissue_corr_coeffient, @@ -174,10 +191,11 @@ def tissue_correlation_for_trait_list(primary_tissue_vals: List, return lit_corr_result -def fetch_lit_correlation_data(database, - input_mouse_gene_id: Optional[str], - gene_id: str, - mouse_gene_id: Optional[str] = None)->Tuple[str, float]: +def fetch_lit_correlation_data( + database, + 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: @@ -190,12 +208,15 @@ def fetch_lit_correlation_data(database, query_values = (str(mouse_gene_id), str(input_mouse_gene_id)) - results = database.execute( - query_formatter(query, *query_values)).fetchone() - - lit_corr_results = results if results is not None else database.execute( - query_formatter(query, *tuple(reversed(query_values)))).fetchone() - + results = database.execute(query_formatter(query, + *query_values)).fetchone() + lit_corr_results = None + if results is not None: + lit_corr_results = results + else: + lit_corr_results = database.execute( + query_formatter(query, + *tuple(reversed(query_values)))).fetchone() lit_results = (gene_id, lit_corr_results.val)\ if lit_corr_results else (gene_id, 0) return lit_results @@ -203,35 +224,41 @@ def fetch_lit_correlation_data(database, return (gene_id, 0) -def lit_correlation_for_trait_list(database, - target_trait_lists: List, - species: Optional[str] = None, - trait_gene_id: Optional[str] = None)->List: +def lit_correlation_for_trait_list( + database, + 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( - database=database, species=species, gene_id=trait_gene_id) + this_trait_mouse_gene_id = map_to_mouse_gene_id(database=database, + species=species, + gene_id=trait_gene_id) for trait in target_trait_lists: target_trait_gene_id = trait.get("gene_id") if target_trait_gene_id: target_mouse_gene_id = map_to_mouse_gene_id( - database=database, species=species, gene_id=target_trait_gene_id) + database=database, + species=species, + gene_id=target_trait_gene_id) fetched_corr_data = fetch_lit_correlation_data( - database=database, input_mouse_gene_id=this_trait_mouse_gene_id, - gene_id=target_trait_gene_id, mouse_gene_id=target_mouse_gene_id) + database=database, + 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)) + dict_results = dict(zip(("gene_id", "lit_corr"), + fetched_corr_data)) fetched_lit_corr_results.append(dict_results) return fetched_lit_corr_results -def query_formatter(query_string: str, * query_values): +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 """ @@ -240,11 +267,12 @@ def query_formatter(query_string: str, * query_values): return results -def map_to_mouse_gene_id(database, species: Optional[str], gene_id: Optional[str])->Optional[str]: +def map_to_mouse_gene_id(database, 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""" - # AK:xtodo move the code for checking nullity out of thing functions bug while\ - # method for string + # AK:xtodo move the code for checking nullity out of thing functions bug + # while method for string if None in (species, gene_id): return None if species == "mouse": @@ -256,34 +284,36 @@ def map_to_mouse_gene_id(database, species: Optional[str], gene_id: Optional[str query_values = (species, gene_id) - results = database.execute( - query_formatter(query, *query_values)).fetchone() + results = database.execute(query_formatter(query, + *query_values)).fetchone() mouse_gene_id = results.mouse if results is not None else None return mouse_gene_id -def compute_all_lit_correlation(database_instance, trait_lists: List, species: str, gene_id): - """function that acts as an abstraction for lit_correlation_for_trait_list""" +def compute_all_lit_correlation(database_instance, trait_lists: List, + species: str, gene_id): + """Function that acts as an abstraction for + lit_correlation_for_trait_list""" # xtodo to be refactored - lit_results = lit_correlation_for_trait_list(database=database_instance, - target_trait_lists=trait_lists, - species=species, - trait_gene_id=gene_id - ) + lit_results = lit_correlation_for_trait_list( + database=database_instance, + target_trait_lists=trait_lists, + species=species, + trait_gene_id=gene_id) - return { - "lit_results": lit_results - } + return {"lit_results": lit_results} def compute_all_tissue_correlation(primary_tissue_dict: dict, target_tissues_dict_list: List, corr_method: str): - """function acts as an abstraction for tissue_correlation_for_trait_list\ - required input are target tissue object and primary tissue trait """ + """Function acts as an abstraction for tissue_correlation_for_trait_list\ + required input are target tissue object and primary tissue trait + + """ tissues_results = {} @@ -296,9 +326,10 @@ def compute_all_tissue_correlation(primary_tissue_dict: dict, target_tissue_vals = target_tissue_obj.get("tissue_values") - tissue_result = tissue_correlation_for_trait_list(primary_tissue_vals=primary_tissue_vals, - target_tissues_values=target_tissue_vals, - corr_method=corr_method) + tissue_result = tissue_correlation_for_trait_list( + primary_tissue_vals=primary_tissue_vals, + target_tissues_values=target_tissue_vals, + corr_method=corr_method) tissues_results[trait_id] = tissue_result |