diff options
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/lmm.py | 50 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/runlmm.py | 2 |
2 files changed, 24 insertions, 28 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py index 88ca6a7f..9e25f56d 100644 --- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py +++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py @@ -81,8 +81,7 @@ def run_human(pheno_vector, covariate_matrix, plink_input_file, kinship_matrix, - refit=False, - tempdata=None): + refit=False): v = np.isnan(pheno_vector) keep = True - v @@ -262,23 +261,19 @@ def human_association(snp, def run_other_old(pheno_vector, genotype_matrix, restricted_max_likelihood=True, - refit=False, - tempdata=None # <---- can not be None - ): + refit=False): """Takes the phenotype vector and genotype matrix and returns a set of p-values and t-statistics restricted_max_likelihood -- whether to use restricted max likelihood; True or False refit -- whether to refit the variance component for each marker - temp_data -- TempData object that stores the progress for each major step of the - calculations ("calculate_kinship" and "GWAS" take the majority of time) """ print("Running the original LMM engine in run_other (old)") print("REML=",restricted_max_likelihood," REFIT=",refit) with Bench("Calculate Kinship"): - kinship_matrix,genotype_matrix = calculate_kinship_new(genotype_matrix, tempdata) + kinship_matrix,genotype_matrix = calculate_kinship_new(genotype_matrix) print("kinship_matrix: ", pf(kinship_matrix)) print("kinship_matrix.shape: ", pf(kinship_matrix.shape)) @@ -297,24 +292,19 @@ def run_other_old(pheno_vector, genotype_matrix, kinship_matrix, restricted_max_likelihood=True, - refit=False, - temp_data=tempdata) + refit=False) Bench().report() return p_values, t_stats def run_other_new(pheno_vector, genotype_matrix, restricted_max_likelihood=True, - refit=False, - tempdata=None # <---- can not be None - ): + refit=False): """Takes the phenotype vector and genotype matrix and returns a set of p-values and t-statistics restricted_max_likelihood -- whether to use restricted max likelihood; True or False refit -- whether to refit the variance component for each marker - temp_data -- TempData object that stores the progress for each major step of the - calculations ("calculate_kinship" and "GWAS" take the majority of time) """ @@ -332,7 +322,7 @@ def run_other_new(pheno_vector, # G = np.apply_along_axis( genotype.normalize, axis=1, arr=G) with Bench("Calculate Kinship"): - K,G = calculate_kinship_new(G, tempdata) + K,G = calculate_kinship_new(G) print("kinship_matrix: ", pf(K)) print("kinship_matrix.shape: ", pf(K.shape)) @@ -815,25 +805,24 @@ def gwas_without_redis(species,k,y,geno,cov,reml,refit,inputfn,new_code): if species == "human" : print('kinship', k ) ps, ts = run_human(pheno_vector = y, - covariate_matrix = cov, - plink_input_file = inputfn, - kinship_matrix = k, - refit = refit, tempdata=tempdata) + covariate_matrix = cov, + plink_input_file = inputfn, + kinship_matrix = k, + refit = refit) else: print('geno', geno.shape, geno) if new_code: ps, ts = run_other_new(pheno_vector = y, - genotype_matrix = geno, - restricted_max_likelihood = reml, - refit = refit, - tempdata = tempdata) + genotype_matrix = geno, + restricted_max_likelihood = reml, + refit = refit) else: ps, ts = run_other_old(pheno_vector = y, genotype_matrix = geno, restricted_max_likelihood = reml, - refit = refit, - tempdata = tempdata) + refit = refit) + return ps,ts def gwas_using_redis(key,species,new_code=True): """ @@ -853,7 +842,14 @@ def gwas_using_redis(key,species,new_code=True): debug("Updating REDIS percent_complete=%d" % (round(i*100.0/total))) progress_set_func(update_tempdata) - ps,ts = gwas_without_redis(species,np.array(params['kinship_matrix']),np.array(params['pheno_vector']),np.array(params['genotype_matrix']),np.array(params['covariate_matrix']),params['restricted_max_likelihood'],params['refit'],params['input_file_name'],new_code) + def narray(key): + print(key) + v = params[key] + if v is not None: + v = np.array(v) + return v + + ps,ts = gwas_without_redis(species,narray('kinship_matrix'),narray('pheno_vector'),narray('genotype_matrix'),narray('covariate_matrix'),params['restricted_max_likelihood'],params['refit'],params['input_file_name'],new_code) results_key = "pylmm:results:" + params['temp_uuid'] diff --git a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py index ab698e41..3801529e 100644 --- a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py +++ b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py @@ -200,7 +200,7 @@ elif cmd == 'kinship': print "Genotype",G.shape, "\n", G print "first Kinship method",K.shape,"\n",K k1 = round(K[0][0],4) - K2,G = calculate_kinship_new(np.copy(G).T,temp_data=None) + K2,G = calculate_kinship_new(np.copy(G).T) print "Genotype",G.shape, "\n", G print "GN2 Kinship method",K2.shape,"\n",K2 k2 = round(K2[0][0],4) |