diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/process_plink.py | 127 |
1 files changed, 0 insertions, 127 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/process_plink.py b/wqflask/wqflask/my_pylmm/pyLMM/process_plink.py deleted file mode 100644 index e47c18e1..00000000 --- a/wqflask/wqflask/my_pylmm/pyLMM/process_plink.py +++ /dev/null @@ -1,127 +0,0 @@ -from __future__ import absolute_import, print_function, division - -import sys -sys.path.append("../../..") - -print("sys.path: ", sys.path) - -import numpy as np - -import zlib -import cPickle as pickle -import redis -Redis = redis.Redis() - -import lmm - -class ProcessLmmChunk(object): - - def __init__(self): - self.get_snp_data() - self.get_lmm_vars() - - keep = self.trim_matrices() - - self.do_association(keep) - - print("p_values is: ", self.p_values) - - def get_snp_data(self): - plink_pickled = zlib.decompress(Redis.lpop("plink_inputs")) - plink_data = pickle.loads(plink_pickled) - - self.snps = np.array(plink_data['result']) - self.identifier = plink_data['identifier'] - - def get_lmm_vars(self): - lmm_vars_pickled = Redis.hget(self.identifier, "lmm_vars") - lmm_vars = pickle.loads(lmm_vars_pickled) - - self.pheno_vector = np.array(lmm_vars['pheno_vector']) - self.covariate_matrix = np.array(lmm_vars['covariate_matrix']) - self.kinship_matrix = np.array(lmm_vars['kinship_matrix']) - - def trim_matrices(self): - v = np.isnan(self.pheno_vector) - keep = True - v - keep = keep.reshape((len(keep),)) - - if v.sum(): - self.pheno_vector = self.pheno_vector[keep] - self.covariate_matrix = self.covariate_matrix[keep,:] - self.kinship_matrix = self.kinship_matrix[keep,:][:,keep] - - return keep - - def do_association(self, keep): - n = self.kinship_matrix.shape[0] - lmm_ob = lmm.LMM(self.pheno_vector, - self.kinship_matrix, - self.covariate_matrix) - lmm_ob.fit() - - self.p_values = [] - - for snp in self.snps: - snp = snp[0] - p_value, t_stat = lmm.human_association(snp, - n, - keep, - lmm_ob, - self.pheno_vector, - self.covariate_matrix, - self.kinship_matrix, - False) - - self.p_values.append(p_value) - - -#plink_pickled = zlib.decompress(Redis.lpop("plink_inputs")) -# -#plink_data = pickle.loads(plink_pickled) -#result = np.array(plink_data['result']) -#print("snp size is: ", result.shape) -#identifier = plink_data['identifier'] -# -#lmm_vars_pickled = Redis.hget(identifier, "lmm_vars") -#lmm_vars = pickle.loads(lmm_vars_pickled) -# -#pheno_vector = np.array(lmm_vars['pheno_vector']) -#covariate_matrix = np.array(lmm_vars['covariate_matrix']) -#kinship_matrix = np.array(lmm_vars['kinship_matrix']) -# -#v = np.isnan(pheno_vector) -#keep = True - v -#keep = keep.reshape((len(keep),)) -#print("keep is: ", keep) -# -#if v.sum(): -# pheno_vector = pheno_vector[keep] -# covariate_matrix = covariate_matrix[keep,:] -# kinship_matrix = kinship_matrix[keep,:][:,keep] -# -#n = kinship_matrix.shape[0] -#print("n is: ", n) -#lmm_ob = lmm.LMM(pheno_vector, -# kinship_matrix, -# covariate_matrix) -#lmm_ob.fit() -# -#p_values = [] -# -#for snp in result: -# snp = snp[0] -# p_value, t_stat = lmm.human_association(snp, -# n, -# keep, -# lmm_ob, -# pheno_vector, -# covariate_matrix, -# kinship_matrix, -# False) -# -# p_values.append(p_value) - - - - |