diff options
Diffstat (limited to 'gn3/correlation/show_corr_results.py')
| -rw-r--r-- | gn3/correlation/show_corr_results.py | 735 |
1 files changed, 0 insertions, 735 deletions
diff --git a/gn3/correlation/show_corr_results.py b/gn3/correlation/show_corr_results.py deleted file mode 100644 index 55d8366..0000000 --- a/gn3/correlation/show_corr_results.py +++ /dev/null @@ -1,735 +0,0 @@ -"""module contains code for doing correlation""" - -import json -import collections -import numpy -import scipy.stats -import rpy2.robjects as ro -from flask import g -from gn3.base.data_set import create_dataset -from gn3.utility.db_tools import escape -from gn3.utility.helper_functions import get_species_dataset_trait -from gn3.utility.corr_result_helpers import normalize_values -from gn3.base.trait import create_trait -from gn3.utility import hmac -from . import correlation_functions - - -class CorrelationResults: - """class for computing correlation""" - # pylint: disable=too-many-instance-attributes - # pylint:disable=attribute-defined-outside-init - - def __init__(self, start_vars): - self.assertion_for_start_vars(start_vars) - - @staticmethod - def assertion_for_start_vars(start_vars): - # pylint: disable = E, W, R, C - - # should better ways to assert the variables - # example includes sample - assert("corr_type" in start_vars) - assert(isinstance(start_vars['corr_type'], str)) - # example includes pearson - assert('corr_sample_method' in start_vars) - assert('corr_dataset' in start_vars) - # means the limit - assert('corr_return_results' in start_vars) - - if "loc_chr" in start_vars: - assert('min_loc_mb' in start_vars) - assert('max_loc_mb' in start_vars) - - def get_formatted_corr_type(self): - """method to formatt corr_types""" - self.formatted_corr_type = "" - if self.corr_type == "lit": - self.formatted_corr_type += "Literature Correlation " - elif self.corr_type == "tissue": - self.formatted_corr_type += "Tissue Correlation " - elif self.corr_type == "sample": - self.formatted_corr_type += "Genetic Correlation " - - if self.corr_method == "pearson": - self.formatted_corr_type += "(Pearson's r)" - elif self.corr_method == "spearman": - self.formatted_corr_type += "(Spearman's rho)" - elif self.corr_method == "bicor": - self.formatted_corr_type += "(Biweight r)" - - def process_samples(self, start_vars, sample_names, excluded_samples=None): - """method to process samples""" - - - if not excluded_samples: - excluded_samples = () - - sample_val_dict = json.loads(start_vars["sample_vals"]) - print(sample_val_dict) - if sample_names is None: - raise NotImplementedError - - for sample in sample_names: - if sample not in excluded_samples: - value = sample_val_dict[sample] - - if not value.strip().lower() == "x": - self.sample_data[str(sample)] = float(value) - - def do_tissue_correlation_for_trait_list(self, tissue_dataset_id=1): - """Given a list of correlation results (self.correlation_results),\ - gets the tissue correlation value for each""" - # pylint: disable = E, W, R, C - - # Gets tissue expression values for the primary trait - primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=[self.this_trait.symbol]) - - if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: - primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower( - )] - gene_symbol_list = [ - trait.symbol for trait in self.correlation_results if trait.symbol] - - corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=gene_symbol_list) - - for trait in self.correlation_results: - if trait.symbol and trait.symbol.lower() in corr_result_tissue_vals_dict: - this_trait_tissue_values = corr_result_tissue_vals_dict[trait.symbol.lower( - )] - - result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, - this_trait_tissue_values, - self.corr_method) - - trait.tissue_corr = result[0] - trait.tissue_pvalue = result[2] - - def do_lit_correlation_for_trait_list(self): - # pylint: disable = E, W, R, C - - input_trait_mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), self.this_trait.geneid) - - for trait in self.correlation_results: - - if trait.geneid: - trait.mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), trait.geneid) - else: - trait.mouse_gene_id = None - - if trait.mouse_gene_id and str(trait.mouse_gene_id).find(";") == -1: - result = g.db.execute( - """SELECT value - FROM LCorrRamin3 - WHERE GeneId1='%s' and - GeneId2='%s' - """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id))) - ).fetchone() - if not result: - result = g.db.execute("""SELECT value - FROM LCorrRamin3 - WHERE GeneId2='%s' and - GeneId1='%s' - """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id))) - ).fetchone() - - if result: - lit_corr = result.value - trait.lit_corr = lit_corr - else: - trait.lit_corr = 0 - else: - trait.lit_corr = 0 - - def do_lit_correlation_for_all_traits(self): - """method for lit_correlation for all traits""" - # pylint: disable = E, W, R, C - input_trait_mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), self.this_trait.geneid) - - lit_corr_data = {} - for trait, gene_id in list(self.trait_geneid_dict.items()): - mouse_gene_id = self.convert_to_mouse_gene_id( - self.dataset.group.species.lower(), gene_id) - - if mouse_gene_id and str(mouse_gene_id).find(";") == -1: - #print("gene_symbols:", input_trait_mouse_gene_id + " / " + mouse_gene_id) - result = g.db.execute( - """SELECT value - FROM LCorrRamin3 - WHERE GeneId1='%s' and - GeneId2='%s' - """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id)) - ).fetchone() - if not result: - result = g.db.execute("""SELECT value - FROM LCorrRamin3 - WHERE GeneId2='%s' and - GeneId1='%s' - """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id)) - ).fetchone() - if result: - #print("result:", result) - lit_corr = result.value - lit_corr_data[trait] = [gene_id, lit_corr] - else: - lit_corr_data[trait] = [gene_id, 0] - else: - lit_corr_data[trait] = [gene_id, 0] - - lit_corr_data = collections.OrderedDict(sorted(list(lit_corr_data.items()), - key=lambda t: -abs(t[1][1]))) - - return lit_corr_data - - def do_tissue_correlation_for_all_traits(self, tissue_dataset_id=1): - # Gets tissue expression values for the primary trait - # pylint: disable = E, W, R, C - primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=[self.this_trait.symbol]) - - if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: - primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower( - )] - - #print("trait_gene_symbols: ", pf(trait_gene_symbols.values())) - corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=list(self.trait_symbol_dict.values())) - - #print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict)) - - #print("trait_gene_symbols: ", pf(trait_gene_symbols)) - - tissue_corr_data = {} - for trait, symbol in list(self.trait_symbol_dict.items()): - if symbol and symbol.lower() in corr_result_tissue_vals_dict: - this_trait_tissue_values = corr_result_tissue_vals_dict[symbol.lower( - )] - - result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, - this_trait_tissue_values, - self.corr_method) - - tissue_corr_data[trait] = [symbol, result[0], result[2]] - - tissue_corr_data = collections.OrderedDict(sorted(list(tissue_corr_data.items()), - key=lambda t: -abs(t[1][1]))) - - def get_sample_r_and_p_values(self, trait, target_samples): - """Calculates the sample r (or rho) and p-value - - Given a primary trait and a target trait's sample values, - calculates either the pearson r or spearman rho and the p-value - using the corresponding scipy functions. - - """ - # pylint: disable = E, W, R, C - self.this_trait_vals = [] - target_vals = [] - - for index, sample in enumerate(self.target_dataset.samplelist): - if sample in self.sample_data: - sample_value = self.sample_data[sample] - target_sample_value = target_samples[index] - self.this_trait_vals.append(sample_value) - target_vals.append(target_sample_value) - - self.this_trait_vals, target_vals, num_overlap = normalize_values( - self.this_trait_vals, target_vals) - - if num_overlap > 5: - # ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/ - if self.corr_method == 'bicor': - sample_r, sample_p = do_bicor( - self.this_trait_vals, target_vals) - - elif self.corr_method == 'pearson': - sample_r, sample_p = scipy.stats.pearsonr( - self.this_trait_vals, target_vals) - - else: - sample_r, sample_p = scipy.stats.spearmanr( - self.this_trait_vals, target_vals) - - if numpy.isnan(sample_r): - pass - - else: - - self.correlation_data[trait] = [ - sample_r, sample_p, num_overlap] - - def convert_to_mouse_gene_id(self, species=None, gene_id=None): - """If the species is rat or human, translate the gene_id to the mouse geneid - - If there is no input gene_id or there's no corresponding mouse gene_id, return None - - """ - if not gene_id: - return None - - mouse_gene_id = None - if "species" == "mouse": - mouse_gene_id = gene_id - - elif species == 'rat': - query = """SELECT mouse - FROM GeneIDXRef - WHERE rat='%s'""" % escape(gene_id) - - result = g.db.execute(query).fetchone() - if result != None: - mouse_gene_id = result.mouse - - elif species == "human": - - query = """SELECT mouse - FROM GeneIDXRef - WHERE human='%s'""" % escape(gene_id) - - result = g.db.execute(query).fetchone() - if result != None: - mouse_gene_id = result.mouse - - return mouse_gene_id - - def do_correlation(self, start_vars, create_dataset=create_dataset, - create_trait=create_trait, - get_species_dataset_trait=get_species_dataset_trait): - # pylint: disable = E, W, R, C - # probably refactor start_vars being passed twice - # this method aims to replace the do_correlation but also add dependendency injection - # to enable testing - - # should maybe refactor below code more or less works the same - if start_vars["dataset"] == "Temp": - self.dataset = create_dataset( - dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group']) - - self.trait_id = start_vars["trait_id"] - - self.this_trait = create_trait(dataset=self.dataset, - name=self.trait_id, - cellid=None) - - else: - - get_species_dataset_trait(self, start_vars) - - corr_samples_group = start_vars['corr_samples_group'] - self.sample_data = {} - self.corr_type = start_vars['corr_type'] - self.corr_method = start_vars['corr_sample_method'] - self.min_expr = float( - start_vars["min_expr"]) if start_vars["min_expr"] != "" else None - self.p_range_lower = float( - start_vars["p_range_lower"]) if start_vars["p_range_lower"] != "" else -1.0 - self.p_range_upper = float( - start_vars["p_range_upper"]) if start_vars["p_range_upper"] != "" else 1.0 - - if ("loc_chr" in start_vars and "min_loc_mb" in start_vars and "max_loc_mb" in start_vars): - self.location_type = str(start_vars['location_type']) - self.location_chr = str(start_vars['loc_chr']) - - try: - - # the code is below is basically a temporary fix - self.min_location_mb = int(start_vars['min_loc_mb']) - self.max_location_mb = int(start_vars['max_loc_mb']) - except Exception as e: - self.min_location_mb = None - self.max_location_mb = None - - else: - self.location_type = self.location_chr = self.min_location_mb = self.max_location_mb = None - - self.get_formatted_corr_type() - - self.return_number = int(start_vars['corr_return_results']) - - primary_samples = self.dataset.group.samplelist - - - # The two if statements below append samples to the sample list based upon whether the user - # rselected Primary Samples Only, Other Samples Only, or All Samples - - if self.dataset.group.parlist != None: - primary_samples += self.dataset.group.parlist - - if self.dataset.group.f1list != None: - - primary_samples += self.dataset.group.f1list - - # If either BXD/whatever Only or All Samples, append all of that group's samplelist - - if corr_samples_group != 'samples_other': - - # print("primary samples are *****",primary_samples) - - self.process_samples(start_vars, primary_samples) - - if corr_samples_group != 'samples_primary': - if corr_samples_group == 'samples_other': - primary_samples = [x for x in primary_samples if x not in ( - self.dataset.group.parlist + self.dataset.group.f1list)] - - self.process_samples(start_vars, list(self.this_trait.data.keys()), primary_samples) - - self.target_dataset = create_dataset(start_vars['corr_dataset']) - # when you add code to retrieve the trait_data for target dataset got gets very slow - import time - - init_time = time.time() - self.target_dataset.get_trait_data(list(self.sample_data.keys())) - - aft_time = time.time() - init_time - - self.header_fields = get_header_fields( - self.target_dataset.type, self.corr_method) - - if self.target_dataset.type == "ProbeSet": - self.filter_cols = [7, 6] - - elif self.target_dataset.type == "Publish": - self.filter_cols = [6, 0] - - else: - self.filter_cols = [4, 0] - - self.correlation_results = [] - - self.correlation_data = {} - - if self.corr_type == "tissue": - self.trait_symbol_dict = self.dataset.retrieve_genes("Symbol") - - tissue_corr_data = self.do_tissue_correlation_for_all_traits() - if tissue_corr_data != None: - for trait in list(tissue_corr_data.keys())[:self.return_number]: - self.get_sample_r_and_p_values( - trait, self.target_dataset.trait_data[trait]) - else: - for trait, values in list(self.target_dataset.trait_data.items()): - self.get_sample_r_and_p_values(trait, values) - - elif self.corr_type == "lit": - self.trait_geneid_dict = self.dataset.retrieve_genes("GeneId") - lit_corr_data = self.do_lit_correlation_for_all_traits() - - for trait in list(lit_corr_data.keys())[:self.return_number]: - self.get_sample_r_and_p_values( - trait, self.target_dataset.trait_data[trait]) - - elif self.corr_type == "sample": - for trait, values in list(self.target_dataset.trait_data.items()): - self.get_sample_r_and_p_values(trait, values) - - self.correlation_data = collections.OrderedDict(sorted(list(self.correlation_data.items()), - key=lambda t: -abs(t[1][0]))) - - # ZS: Convert min/max chromosome to an int for the location range option - - """ - took 20.79 seconds took compute all the above majority of time taken on retrieving target dataset trait - info - """ - - initial_time_chr = time.time() - - range_chr_as_int = None - for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()): - if 'loc_chr' in start_vars: - if chr_info.name == self.location_chr: - range_chr_as_int = order_id - - for _trait_counter, trait in enumerate(list(self.correlation_data.keys())[:self.return_number]): - trait_object = create_trait( - dataset=self.target_dataset, name=trait, get_qtl_info=True, get_sample_info=False) - if not trait_object: - continue - - chr_as_int = 0 - for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()): - if self.location_type == "highest_lod": - if chr_info.name == trait_object.locus_chr: - chr_as_int = order_id - else: - if chr_info.name == trait_object.chr: - chr_as_int = order_id - - if (float(self.correlation_data[trait][0]) >= self.p_range_lower and - float(self.correlation_data[trait][0]) <= self.p_range_upper): - - if (self.target_dataset.type == "ProbeSet" or self.target_dataset.type == "Publish") and bool(trait_object.mean): - if (self.min_expr != None) and (float(trait_object.mean) < self.min_expr): - continue - - if range_chr_as_int != None and (chr_as_int != range_chr_as_int): - continue - if self.location_type == "highest_lod": - if (self.min_location_mb != None) and (float(trait_object.locus_mb) < float(self.min_location_mb)): - continue - if (self.max_location_mb != None) and (float(trait_object.locus_mb) > float(self.max_location_mb)): - continue - else: - if (self.min_location_mb != None) and (float(trait_object.mb) < float(self.min_location_mb)): - continue - if (self.max_location_mb != None) and (float(trait_object.mb) > float(self.max_location_mb)): - continue - - (trait_object.sample_r, - trait_object.sample_p, - trait_object.num_overlap) = self.correlation_data[trait] - - # Set some sane defaults - trait_object.tissue_corr = 0 - trait_object.tissue_pvalue = 0 - trait_object.lit_corr = 0 - if self.corr_type == "tissue" and tissue_corr_data != None: - trait_object.tissue_corr = tissue_corr_data[trait][1] - trait_object.tissue_pvalue = tissue_corr_data[trait][2] - elif self.corr_type == "lit": - trait_object.lit_corr = lit_corr_data[trait][1] - - self.correlation_results.append(trait_object) - - """ - above takes time with respect to size of traits i.e n=100,500,.....t_size - """ - - if self.corr_type != "lit" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet": - # self.do_lit_correlation_for_trait_list() - self.do_lit_correlation_for_trait_list() - - if self.corr_type != "tissue" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet": - self.do_tissue_correlation_for_trait_list() - # self.do_lit_correlation_for_trait_list() - - self.json_results = generate_corr_json( - self.correlation_results, self.this_trait, self.dataset, self.target_dataset) - - # org mode by bons - - # DVORAKS - # klavaro for touch typing - # archwiki for documentation - # exwm for window manager ->13 - - # will fit perfectly with genenetwork 2 with change of anything if return self - - # alternative for this - return self.json_results - # return { - # # "Results": "succeess", - # # "return_number": self.return_number, - # # "primary_samples": primary_samples, - # # "time_taken": 12, - # # "correlation_data": self.correlation_data, - # "correlation_json": self.json_results - # } - - -def do_bicor(this_trait_vals, target_trait_vals): - # pylint: disable = E, W, R, C - r_library = ro.r["library"] # Map the library function - r_options = ro.r["options"] # Map the options function - - r_library("WGCNA") - r_bicor = ro.r["bicorAndPvalue"] # Map the bicorAndPvalue function - - r_options(stringsAsFactors=False) - - this_vals = ro.Vector(this_trait_vals) - target_vals = ro.Vector(target_trait_vals) - - the_r, the_p, _fisher_transform, _the_t, _n_obs = [ - numpy.asarray(x) for x in r_bicor(x=this_vals, y=target_vals)] - - return the_r, the_p - - -def get_header_fields(data_type, corr_method): - """function to get header fields when doing correlation""" - if data_type == "ProbeSet": - if corr_method == "spearman": - - header_fields = ['Index', - 'Record', - 'Symbol', - 'Description', - 'Location', - 'Mean', - 'Sample rho', - 'N', - 'Sample p(rho)', - 'Lit rho', - 'Tissue rho', - 'Tissue p(rho)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - else: - header_fields = ['Index', - 'Record', - 'Abbreviation', - 'Description', - 'Mean', - 'Authors', - 'Year', - 'Sample r', - 'N', - 'Sample p(r)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - elif data_type == "Publish": - if corr_method == "spearman": - - header_fields = ['Index', - 'Record', - 'Abbreviation', - 'Description', - 'Mean', - 'Authors', - 'Year', - 'Sample rho', - 'N', - 'Sample p(rho)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - else: - header_fields = ['Index', - 'Record', - 'Abbreviation', - 'Description', - 'Mean', - 'Authors', - 'Year', - 'Sample r', - 'N', - 'Sample p(r)', - 'Max LRS', - 'Max LRS Location', - 'Additive Effect'] - - else: - if corr_method == "spearman": - header_fields = ['Index', - 'ID', - 'Location', - 'Sample rho', - 'N', - 'Sample p(rho)'] - - else: - header_fields = ['Index', - 'ID', - 'Location', - 'Sample r', - 'N', - 'Sample p(r)'] - - return header_fields - - -def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False): - """function to generate corr json data""" - #todo refactor this function - results_list = [] - for i, trait in enumerate(corr_results): - if trait.view == False: - continue - results_dict = {} - results_dict['index'] = i + 1 - results_dict['trait_id'] = trait.name - results_dict['dataset'] = trait.dataset.name - results_dict['hmac'] = hmac.data_hmac( - '{}:{}'.format(trait.name, trait.dataset.name)) - if target_dataset.type == "ProbeSet": - results_dict['symbol'] = trait.symbol - results_dict['description'] = "N/A" - results_dict['location'] = trait.location_repr - results_dict['mean'] = "N/A" - results_dict['additive'] = "N/A" - if bool(trait.description_display): - results_dict['description'] = trait.description_display - if bool(trait.mean): - results_dict['mean'] = f"{float(trait.mean):.3f}" - try: - results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = trait.LRS_location_repr - if bool(trait.additive): - results_dict['additive'] = f"{float(trait.additive):.3f}" - results_dict['sample_r'] = f"{float(trait.sample_r):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait.sample_p):.3e}" - results_dict['lit_corr'] = "--" - results_dict['tissue_corr'] = "--" - results_dict['tissue_pvalue'] = "--" - if bool(trait.lit_corr): - results_dict['lit_corr'] = f"{float(trait.lit_corr):.3f}" - if bool(trait.tissue_corr): - results_dict['tissue_corr'] = f"{float(trait.tissue_corr):.3f}" - results_dict['tissue_pvalue'] = f"{float(trait.tissue_pvalue):.3e}" - elif target_dataset.type == "Publish": - results_dict['abbreviation_display'] = "N/A" - results_dict['description'] = "N/A" - results_dict['mean'] = "N/A" - results_dict['authors_display'] = "N/A" - results_dict['additive'] = "N/A" - if for_api: - results_dict['pubmed_id'] = "N/A" - results_dict['year'] = "N/A" - else: - results_dict['pubmed_link'] = "N/A" - results_dict['pubmed_text'] = "N/A" - - if bool(trait.abbreviation): - results_dict['abbreviation_display'] = trait.abbreviation - if bool(trait.description_display): - results_dict['description'] = trait.description_display - if bool(trait.mean): - results_dict['mean'] = f"{float(trait.mean):.3f}" - if bool(trait.authors): - authors_list = trait.authors.split(',') - if len(authors_list) > 6: - results_dict['authors_display'] = ", ".join( - authors_list[:6]) + ", et al." - else: - results_dict['authors_display'] = trait.authors - if bool(trait.pubmed_id): - if for_api: - results_dict['pubmed_id'] = trait.pubmed_id - results_dict['year'] = trait.pubmed_text - else: - results_dict['pubmed_link'] = trait.pubmed_link - results_dict['pubmed_text'] = trait.pubmed_text - try: - results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" - except: - results_dict['lod_score'] = "N/A" - results_dict['lrs_location'] = trait.LRS_location_repr - if bool(trait.additive): - results_dict['additive'] = f"{float(trait.additive):.3f}" - results_dict['sample_r'] = f"{float(trait.sample_r):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait.sample_p):.3e}" - else: - results_dict['location'] = trait.location_repr - results_dict['sample_r'] = f"{float(trait.sample_r):.3f}" - results_dict['num_overlap'] = trait.num_overlap - results_dict['sample_p'] = f"{float(trait.sample_p):.3e}" - - results_list.append(results_dict) - - return json.dumps(results_list) |