diff options
| -rw-r--r-- | wqflask/wqflask/marker_regression/marker_regression_old.py | 576 |
1 files changed, 0 insertions, 576 deletions
diff --git a/wqflask/wqflask/marker_regression/marker_regression_old.py b/wqflask/wqflask/marker_regression/marker_regression_old.py deleted file mode 100644 index 36331250..00000000 --- a/wqflask/wqflask/marker_regression/marker_regression_old.py +++ /dev/null @@ -1,576 +0,0 @@ -from __future__ import absolute_import, print_function, division - -from base.trait import GeneralTrait -from base import data_set #import create_dataset - -from pprint import pformat as pf - -import string -import sys -import datetime -import os -import collections -import uuid - -import numpy as np -from scipy import linalg - -import cPickle as pickle - -import simplejson as json - -from redis import Redis -Redis = Redis() - -from flask import Flask, g - -from base.trait import GeneralTrait -from base import data_set -from base import species -from base import webqtlConfig -from utility import webqtlUtil -from wqflask.my_pylmm.data import prep_data -from wqflask.my_pylmm.pyLMM import lmm -from wqflask.my_pylmm.pyLMM import input -from utility import helper_functions -from utility import Plot, Bunch -from utility import temp_data - -from utility.benchmark import Bench - - -class MarkerRegression(object): - - def __init__(self, start_vars, temp_uuid): - - helper_functions.get_species_dataset_trait(self, start_vars) - - #tempdata = temp_data.TempData(temp_uuid) - - self.samples = [] # Want only ones with values - self.vals = [] - - for sample in self.dataset.group.samplelist: - value = start_vars['value:' + sample] - self.samples.append(str(sample)) - self.vals.append(value) - - self.mapping_method = start_vars['method'] - self.maf = start_vars['maf'] # Minor allele frequency - print("self.maf:", self.maf) - - self.dataset.group.get_markers() - if self.mapping_method == "gemma": - qtl_results = self.run_gemma() - elif self.mapping_method == "plink": - qtl_results = self.run_plink() - #print("qtl_results:", pf(qtl_results)) - elif self.mapping_method == "pylmm": - print("RUNNING PYLMM") - #self.qtl_results = self.gen_data(tempdata) - qtl_results = self.gen_data(str(temp_uuid)) - else: - print("RUNNING NOTHING") - - self.lod_cutoff = 2 - self.filtered_markers = [] - for marker in qtl_results: - if marker['chr'] > 0: - self.filtered_markers.append(marker) - - #Get chromosome lengths for drawing the manhattan plot - chromosome_mb_lengths = {} - for key in self.species.chromosomes.chromosomes.keys(): - chromosome_mb_lengths[key] = self.species.chromosomes.chromosomes[key].mb_length - - self.js_data = dict( - this_trait = self.this_trait.name, - data_set = self.dataset.name, - maf = self.maf, - chromosomes = chromosome_mb_lengths, - qtl_results = self.filtered_markers, - ) - - def run_gemma(self): - """Generates p-values for each marker using GEMMA""" - - #filename = webqtlUtil.genRandStr("{}_{}_".format(self.dataset.group.name, self.this_trait.name)) - self.gen_pheno_txt_file() - - os.chdir("/home/zas1024/gene/web/gemma") - - gemma_command = './gemma -bfile %s -k output_%s.cXX.txt -lmm 1 -o %s_output' % ( - self.dataset.group.name, - self.dataset.group.name, - self.dataset.group.name) - print("gemma_command:" + gemma_command) - - os.system(gemma_command) - - included_markers, p_values = self.parse_gemma_output() - - self.dataset.group.get_specified_markers(markers = included_markers) - - #for marker in self.dataset.group.markers.markers: - # if marker['name'] not in included_markers: - # print("marker:", marker) - # self.dataset.group.markers.markers.remove(marker) - # #del self.dataset.group.markers.markers[marker] - - self.dataset.group.markers.add_pvalues(p_values) - - return self.dataset.group.markers.markers - - - def parse_gemma_output(self): - included_markers = [] - p_values = [] - with open("/home/zas1024/gene/web/gemma/output/{}_output.assoc.txt".format(self.dataset.group.name)) as output_file: - for line in output_file: - if line.startswith("chr"): - continue - else: - included_markers.append(line.split("\t")[1]) - p_values.append(float(line.split("\t")[10])) - #p_values[line.split("\t")[1]] = float(line.split("\t")[10]) - print("p_values: ", p_values) - return included_markers, p_values - - def gen_pheno_txt_file(self): - """Generates phenotype file for GEMMA""" - - #with open("/home/zas1024/gene/web/gemma/tmp_pheno/{}.txt".format(filename), "w") as outfile: - # for sample, i in enumerate(self.samples): - # print("sample:" + str(i)) - # print("self.vals[i]:" + str(self.vals[sample])) - # outfile.write(str(i) + "\t" + str(self.vals[sample]) + "\n") - - with open("/home/zas1024/gene/web/gemma/{}.fam".format(self.dataset.group.name), "w") as outfile: - for i, sample in enumerate(self.samples): - outfile.write(str(sample) + " " + str(sample) + " 0 0 0 " + str(self.vals[i]) + "\n") - - #def gen_plink_for_gemma(self, filename): - # - # make_bed = "/home/zas1024/plink/plink --file /home/zas1024/plink/%s --noweb --no-fid --no-parents --no-sex --no-pheno --pheno %s%s.txt --out %s%s --make-bed" % (webqtlConfig.HTMLPATH, - # webqtlConfig.HTMLPATH, - # self.dataset.group.name, - # webqtlConfig.TMPDIR, - # filename, - # webqtlConfig.TMPDIR, - # filename) - # - # - - def run_plink(self): - - os.chdir("/home/zas1024/plink") - - plink_output_filename = webqtlUtil.genRandStr("%s_%s_"%(self.dataset.group.name, self.this_trait.name)) - - self.gen_pheno_txt_file_plink(pheno_filename = plink_output_filename) - - plink_command = './plink --noweb --ped %s.ped --no-fid --no-parents --no-sex --no-pheno --map %s.map --pheno %s/%s.txt --pheno-name %s --maf %s --missing-phenotype -9999 --out %s%s --assoc ' % (self.dataset.group.name, self.dataset.group.name, webqtlConfig.TMPDIR, plink_output_filename, self.this_trait.name, self.maf, webqtlConfig.TMPDIR, plink_output_filename) - - os.system(plink_command) - - count, p_values = self.parse_plink_output(plink_output_filename) - #gemma_command = './gemma -bfile %s -k output_%s.cXX.txt -lmm 1 -o %s_output' % ( - # self.dataset.group.name, - # self.dataset.group.name, - # self.dataset.group.name) - #print("gemma_command:" + gemma_command) - # - #os.system(gemma_command) - # - #included_markers, p_values = self.parse_gemma_output() - # - #self.dataset.group.get_specified_markers(markers = included_markers) - - #for marker in self.dataset.group.markers.markers: - # if marker['name'] not in included_markers: - # print("marker:", marker) - # self.dataset.group.markers.markers.remove(marker) - # #del self.dataset.group.markers.markers[marker] - - print("p_values:", pf(p_values)) - - self.dataset.group.markers.add_pvalues(p_values) - - return self.dataset.group.markers.markers - - - def gen_pheno_txt_file_plink(self, pheno_filename = ''): - ped_sample_list = self.get_samples_from_ped_file() - output_file = open("%s%s.txt" % (webqtlConfig.TMPDIR, pheno_filename), "wb") - header = 'FID\tIID\t%s\n' % self.this_trait.name - output_file.write(header) - - new_value_list = [] - - #if valueDict does not include some strain, value will be set to -9999 as missing value - for i, sample in enumerate(ped_sample_list): - try: - value = self.vals[i] - value = str(value).replace('value=','') - value = value.strip() - except: - value = -9999 - - new_value_list.append(value) - - - new_line = '' - for i, sample in enumerate(ped_sample_list): - j = i+1 - value = new_value_list[i] - new_line += '%s\t%s\t%s\n'%(sample, sample, value) - - if j%1000 == 0: - output_file.write(newLine) - new_line = '' - - if new_line: - output_file.write(new_line) - - output_file.close() - - # get strain name from ped file in order - def get_samples_from_ped_file(self): - - os.chdir("/home/zas1024/plink") - - ped_file= open("{}.ped".format(self.dataset.group.name),"r") - line = ped_file.readline() - sample_list=[] - - while line: - lineList = string.split(string.strip(line), '\t') - lineList = map(string.strip, lineList) - - sample_name = lineList[0] - sample_list.append(sample_name) - - line = ped_file.readline() - - return sample_list - - ################################################################ - # Generate Chr list, Chr OrderId and Retrieve Length Information - ################################################################ - #def getChrNameOrderIdLength(self,RISet=''): - # try: - # query = """ - # Select - # Chr_Length.Name,Chr_Length.OrderId,Length from Chr_Length, InbredSet - # where - # Chr_Length.SpeciesId = InbredSet.SpeciesId AND - # InbredSet.Name = '%s' - # Order by OrderId - # """ % (self.dataset.group.name) - # results =g.db.execute(query).fetchall() - # ChrList=[] - # ChrLengthMbList=[] - # ChrNameOrderIdDict={} - # ChrOrderIdNameDict={} - # - # for item in results: - # ChrList.append(item[0]) - # ChrNameOrderIdDict[item[0]]=item[1] # key is chr name, value is orderId - # ChrOrderIdNameDict[item[1]]=item[0] # key is orderId, value is chr name - # ChrLengthMbList.append(item[2]) - # - # except: - # ChrList=[] - # ChrNameOrderIdDict={} - # ChrLengthMbList=[] - # - # return ChrList,ChrNameOrderIdDict,ChrOrderIdNameDict,ChrLengthMbList - - - def parse_plink_output(self, output_filename): - plink_results={} - - threshold_p_value = 0.01 - - result_fp = open("%s%s.qassoc"% (webqtlConfig.TMPDIR, output_filename), "rb") - - header_line = result_fp.readline()# read header line - line = result_fp.readline() - - value_list = [] # initialize value list, this list will include snp, bp and pvalue info - p_value_dict = {} - count = 0 - - while line: - #convert line from str to list - line_list = self.build_line_list(line=line) - - # only keep the records whose chromosome name is in db - if self.species.chromosomes.chromosomes.has_key(int(line_list[0])) and line_list[-1] and line_list[-1].strip()!='NA': - - chr_name = self.species.chromosomes.chromosomes[int(line_list[0])] - snp = line_list[1] - BP = line_list[2] - p_value = float(line_list[-1]) - if threshold_p_value >= 0 and threshold_p_value <= 1: - if p_value < threshold_p_value: - p_value_dict[snp] = p_value - - if plink_results.has_key(chr_name): - value_list = plink_results[chr_name] - - # pvalue range is [0,1] - if threshold_p_value >=0 and threshold_p_value <= 1: - if p_value < threshold_p_value: - value_list.append((snp, BP, p_value)) - count += 1 - - plink_results[chr_name] = value_list - value_list = [] - else: - if threshold_p_value >= 0 and threshold_p_value <= 1: - if p_value < threshold_p_value: - value_list.append((snp, BP, p_value)) - count += 1 - - if value_list: - plink_results[chr_name] = value_list - - value_list=[] - - line = result_fp.readline() - else: - line = result_fp.readline() - - #if p_value_list: - # min_p_value = min(p_value_list) - #else: - # min_p_value = 0 - - return count, p_value_dict - - ###################################################### - # input: line: str,one line read from file - # function: convert line from str to list; - # output: lineList list - ####################################################### - def build_line_list(self, line=None): - - line_list = string.split(string.strip(line),' ')# irregular number of whitespaces between columns - line_list = [item for item in line_list if item <>''] - line_list = map(string.strip, line_list) - - return line_list - - #def gen_data(self, tempdata): - def gen_data(self, temp_uuid): - """Generates p-values for each marker""" - - pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals]) - - #lmm_uuid = str(uuid.uuid4()) - - key = "pylmm:input:" + temp_uuid - print("key is:", pf(key)) - #with Bench("Loading cache"): - # result = Redis.get(key) - - if self.dataset.group.species == "human": - p_values, t_stats = self.gen_human_results(pheno_vector, key, temp_uuid) - #p_values = self.trim_results(p_values) - - else: - print("NOW CWD IS:", os.getcwd()) - genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers] - - no_val_samples = self.identify_empty_samples() - trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples) - - genotype_matrix = np.array(trimmed_genotype_data).T - - #print("pheno_vector: ", pf(pheno_vector)) - #print("genotype_matrix: ", pf(genotype_matrix)) - #print("genotype_matrix.shape: ", pf(genotype_matrix.shape)) - - #params = {"pheno_vector": pheno_vector, - # "genotype_matrix": genotype_matrix, - # "restricted_max_likelihood": True, - # "refit": False, - # "temp_data": tempdata} - - params = dict(pheno_vector = pheno_vector.tolist(), - genotype_matrix = genotype_matrix.tolist(), - restricted_max_likelihood = True, - refit = False, - temp_uuid = temp_uuid, - - # meta data - timestamp = datetime.datetime.now().isoformat(), - ) - - json_params = json.dumps(params) - #print("json_params:", json_params) - Redis.set(key, json_params) - Redis.expire(key, 60*60) - print("before printing command") - - command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key, - "other") - print("command is:", command) - print("after printing command") - - os.system(command) - - #t_stats, p_values = lmm.run(key) - #lmm.run(key) - - json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60) - results = json.loads(json_results[1]) - p_values = [float(result) for result in results['p_values']] - print("p_values:", p_values) - #p_values = self.trim_results(p_values) - t_stats = results['t_stats'] - - #t_stats, p_values = lmm.run( - # pheno_vector, - # genotype_matrix, - # restricted_max_likelihood=True, - # refit=False, - # temp_data=tempdata - #) - #print("p_values:", p_values) - - self.dataset.group.markers.add_pvalues(p_values) - - #self.get_lod_score_cutoff() - - return self.dataset.group.markers.markers - - def trim_results(self, p_values): - print("len_p_values:", len(p_values)) - if len(p_values) > 500: - p_values.sort(reverse=True) - trimmed_values = p_values[:500] - - return trimmed_values - - #def gen_human_results(self, pheno_vector, tempdata): - def gen_human_results(self, pheno_vector, key, temp_uuid): - file_base = os.path.join(webqtlConfig.PYLMM_PATH, self.dataset.group.name) - - plink_input = input.plink(file_base, type='b') - input_file_name = os.path.join(webqtlConfig.SNP_PATH, self.dataset.group.name + ".snps.gz") - - pheno_vector = pheno_vector.reshape((len(pheno_vector), 1)) - covariate_matrix = np.ones((pheno_vector.shape[0],1)) - kinship_matrix = np.fromfile(open(file_base + '.kin','r'),sep=" ") - kinship_matrix.resize((len(plink_input.indivs),len(plink_input.indivs))) - - print("Before creating params") - - params = dict(pheno_vector = pheno_vector.tolist(), - covariate_matrix = covariate_matrix.tolist(), - input_file_name = input_file_name, - kinship_matrix = kinship_matrix.tolist(), - refit = False, - temp_uuid = temp_uuid, - - # meta data - timestamp = datetime.datetime.now().isoformat(), - ) - - print("After creating params") - - json_params = json.dumps(params) - Redis.set(key, json_params) - Redis.expire(key, 60*60) - - print("Before creating the command") - - command = 'python /home/zas1024/gene/wqflask/wqflask/my_pylmm/pyLMM/lmm.py --key {} --species {}'.format(key, - "human") - - print("command is:", command) - - os.system(command) - - json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60) - results = json.loads(json_results[1]) - t_stats = results['t_stats'] - p_values = results['p_values'] - - - #p_values, t_stats = lmm.run_human(key) - - #p_values, t_stats = lmm.run_human( - # pheno_vector, - # covariate_matrix, - # input_file_name, - # kinship_matrix, - # loading_progress=tempdata - # ) - - return p_values, t_stats - - def get_lod_score_cutoff(self): - print("INSIDE GET LOD CUTOFF") - high_qtl_count = 0 - for marker in self.dataset.group.markers.markers: - if marker['lod_score'] > 1: - high_qtl_count += 1 - - if high_qtl_count > 1000: - return 1 - else: - return 0 - - def identify_empty_samples(self): - no_val_samples = [] - for sample_count, val in enumerate(self.vals): - if val == "x": - no_val_samples.append(sample_count) - return no_val_samples - - def trim_genotypes(self, genotype_data, no_value_samples): - trimmed_genotype_data = [] - for marker in genotype_data: - new_genotypes = [] - for item_count, genotype in enumerate(marker): - if item_count in no_value_samples: - continue - try: - genotype = float(genotype) - except ValueError: - genotype = np.nan - pass - new_genotypes.append(genotype) - trimmed_genotype_data.append(new_genotypes) - return trimmed_genotype_data - -def create_snp_iterator_file(group): - plink_file_base = os.path.join(webqtlConfig.PYLMM_PATH, group) - plink_input = input.plink(plink_file_base, type='b') - - data = dict(plink_input = list(plink_input), - numSNPs = plink_input.numSNPs) - - #input_dict = {} - # - #input_dict['plink_input'] = list(plink_input) - #input_dict['numSNPs'] = plink_input.numSNPs - # - - snp_file_base = os.path.join(webqtlConfig.SNP_PATH, group + ".snps.gz") - - with gzip.open(snp_file_base, "wb") as fh: - pickle.dump(data, fh, pickle.HIGHEST_PROTOCOL) - -#if __name__ == '__main__': -# import cPickle as pickle -# import gzip -# create_snp_iterator_file("HLC") - -if __name__ == '__main__': - import cPickle as pickle - import gzip - create_snp_iterator_file("HLC") |