mapper.load_flip(args.mapper)
mapper.load(args.mapper)
phen = Reader('phenotype')
phen.start(args.phenotype[0])
gen = Reader('genotype')
gen.start(args.genotype[0],
hdf5=args.hdf5,
study_name=args.study_name[0],
ID=False)
e = Encoder(args.out)
e.study_name = args.study_name[0]
row_index, ids = study_indexes(phenotype=phen.folder._data,
genotype=gen.folder._data)
with Timer() as t:
e.matrix(len(ids), save=True)
N_snps_read = 0
while True:
with Timer() as t_gen:
genotype = gen.get_next()
if isinstance(genotype, type(None)):
break
flip = mapper.flip[
args.study_name[0]][N_snps_read:N_snps_read +
genotype.shape[0]]
N_snps_read += genotype.shape[0]
flip_index = (flip == -1)
def haseregression(phen,
gen,
cov,
mapper,
Analyser,
maf,
intercept=True,
interaction=None):
g = tuple([i.folder._data for i in gen])
row_index, ids = study_indexes(phenotype=phen.folder._data,
genotype=g,
covariates=cov.folder._data)
if mapper is not None:
SNP = [0, 0, mapper.n_keys]
else:
SNP = [0, 0, 'unknown']
covariates = cov.get_next(index=row_index[2])
a_cov = A_covariates(covariates, intercept=intercept)
while True:
gc.collect()
if mapper is not None:
if mapper.cluster == 'n':
SNPs_index, keys = mapper.get()
else:
ch = mapper.chunk_pop()
if ch is None:
SNPs_index = None
break
SNPs_index, keys = mapper.get(chunk_number=ch)
if isinstance(SNPs_index, type(None)):
break
Analyser.rsid = keys
else:
SNPs_index = None
with Timer() as t:
genotype = merge_genotype(gen, SNPs_index, mapper)
print('time to read and merge genotype {}s'.format(t.secs))
gc.collect()
if genotype is None:
print 'All genotype processed!'
break
SNP[0] += genotype.shape[0]
genotype = genotype[:, row_index[0]]
if mapper is None:
Analyser.rsid = np.array(range(genotype.shape[0]))
MAF = np.mean(genotype, axis=1) / 2
STD = np.std(genotype, axis=1)
if maf != 0:
filter = (MAF > maf) & (MAF < 1 - maf) & (MAF != 0.5)
genotype = genotype[filter, :]
Analyser.MAF = MAF[filter]
Analyser.rsid = Analyser.rsid[filter]
if genotype.shape[0] == 0:
print 'NO SNPs > MAF'
continue
else:
Analyser.MAF = MAF
SNP[1] += genotype.shape[0]
while True:
phenotype = phen.get_next(index=row_index[1])
if isinstance(phenotype, type(None)):
phen.folder.processed = 0
print 'All phenotypes processed!'
break
if phen.permutation:
np.random.shuffle(phenotype)
b_cov = B_covariates(covariates, phenotype, intercept=intercept)
C = C_matrix(phenotype)
if interaction is not None:
pass
a_test = A_tests(covariates, genotype, intercept=intercept)
a_inv = A_inverse(a_cov, a_test)
N_con = a_inv.shape[1] - 1
DF = (phenotype.shape[0] - a_inv.shape[1])
b4 = B4(phenotype, genotype)
t_stat, SE = HASE(b4, a_inv, b_cov, C, N_con, DF)
print('Read {}, processed {}, total {}'.format(
SNP[0], SNP[1], SNP[2]))
Analyser.t_stat = t_stat
Analyser.SE = SE
if mapper is not None and mapper.cluster == 'y':
Analyser.cluster = True
Analyser.chunk = ch
Analyser.node = mapper.node[1]
if phen.permutation:
Analyser.permutation = True
Analyser.save_result(
phen.folder._data.names[phen.folder._data.start:phen.folder.
_data.finish])
t_stat = None
Analyser.t_stat = None
del b4
del C
del b_cov
del a_inv
del a_test
del t_stat
gc.collect()
if Analyser.cluster:
np.save(
os.path.join(Analyser.out,
str(Analyser.node) + '_node_RSID.npy'),
Analyser.rsid_dic)
def partial_derivatives(save_path=None,COV=None,PHEN=None, GEN=None,
MAP=None, MAF=None, R2=None, B4_flag=False, study_name=None,intercept=True):
row_index, ids = study_indexes(phenotype=PHEN.folder._data,genotype=GEN.folder._data,covariates=COV.folder._data)
metadata={}
#TODO (mid) add parameter to compute PD only for new phenotypes or cov
metadata['id']=ids
metadata['MAF']=[]
metadata['filter']=[]
metadata['names']=[] #TODO (low) change to cov_names
metadata['phenotype']=[]
b_cov=[]
C=[]
a_test=[]
b4=[]
covariates=COV.get_next(index=row_index[2])
if intercept:
metadata['names'].append(study_name+ '_intercept')
metadata['names']=metadata['names']+[ study_name+ '_' + i for i in COV.folder._data.get_names() ]
a_cov=A_covariates(covariates,intercept=intercept)
np.save(os.path.join(save_path,study_name+'_a_cov.npy'),a_cov)
i=1
with Timer() as t_phen:
while True:
phenotype=PHEN.get_next(index=row_index[1])
if isinstance(phenotype, type(None)):
b_cov=np.concatenate(b_cov, axis=1)
C=np.concatenate(C, axis=0)
np.save(os.path.join(save_path,study_name+'_b_cov.npy'),b_cov)
np.save(os.path.join(save_path,study_name+'_C.npy'),C)
break
metadata['phenotype']=metadata['phenotype']+ list(PHEN.folder._data.get_names())
b_cov.append(B_covariates(covariates,phenotype,intercept=intercept))
C.append(C_matrix(phenotype))
print ('Time to PD phenotype {} is {} s'.format(np.array(C).shape, t_phen.secs))
N_snps_read=0
while True:
with Timer() as t_gen:
genotype=GEN.get_next()
if isinstance(genotype, type(None)):
np.save(os.path.join(save_path,study_name+'_a_test.npy'), np.concatenate(a_test) )
np.save(os.path.join(save_path,study_name+'_metadata.npy'),metadata)
if B4_flag:
b4=np.concatenate(b4, axis=0)
np.save(os.path.join(save_path,study_name+'_b4.npy'),b4)
break
flip = MAP.flip[N_snps_read:N_snps_read + genotype.shape[0], 0]
N_snps_read += genotype.shape[0]
flip_index=(flip==-1)
genotype=np.apply_along_axis(lambda x: flip*(x-2*flip_index) ,0,genotype)
genotype=genotype[:,row_index[0]]
maf=np.mean(genotype, axis=1)/2
metadata['MAF']=metadata['MAF']+list(maf)
#TODO (low) add interaction
a_test.append(A_tests(covariates,genotype,intercept=intercept))
if B4_flag:
#works only when all phenotypes in one chunk, if not, do not use this option!
#it would use to much disk space anyway
if len([f for f in PHEN.folder.files if f!='info_dic.npy' ])>1:
print 'pd_full flag disabled!'
B4_flag=False
continue
PHEN.folder.processed=0
if isinstance(phenotype, type(None)):
phenotype=PHEN.get_next(index=row_index[1])
b4.append(B4(phenotype,genotype))
print ('Time to PD genotype {} is {} s'.format(genotype.shape, t_gen.secs))
def haseregression(phen,gen,cov, mapper, Analyser, maf,intercept=True):
g=tuple( [i.folder._data for i in gen ] )
row_index, ids = study_indexes(phenotype=phen.folder._data,
genotype=g,
covariates=cov.folder._data)
if mapper is not None:
SNP=[0,0,mapper.n_keys]
else:
SNP=[0,0,'unknown']
covariates=cov.get_next(index=row_index[2])
a_cov=A_covariates(covariates,intercept=intercept)
while True:
gc.collect()
if mapper is not None:
if mapper.cluster=='n':
SNPs_index, keys=mapper.get()
else:
ch=mapper.chunk_pop()
if ch is None:
SNPs_index=None
break
print ch
SNPs_index, keys=mapper.get(chunk_number=ch)
if isinstance(SNPs_index, type(None)):
break
Analyser.rsid=keys
else:
SNPs_index=None
with Timer() as t:
genotype=merge_genotype(gen, SNPs_index, mapper)
print ('time to read and merge genotype {}s'.format(t.secs))
gc.collect()
if genotype is None:
print 'All genotype processed!'
break
SNP[0]+=genotype.shape[0]
genotype=genotype[:,row_index[0]]
if mapper is None:
Analyser.rsid=np.array(range(genotype.shape[0]))
MAF=np.mean(genotype, axis=1)/2
if maf!=0:
filter=(MAF>maf) & (MAF<1-maf) & (MAF!=0.5)
genotype=genotype[filter,:]
Analyser.MAF=MAF[filter]
Analyser.rsid=Analyser.rsid[filter]
if genotype.shape[0]==0:
print 'NO SNPs > MAF'
continue
else:
Analyser.MAF=MAF
SNP[1]+=genotype.shape[0]
while True:
phenotype=phen.get_next(index=row_index[1])
if isinstance(phenotype, type(None)):
phen.folder.processed=0
print 'All phenotypes processed!'
break
if phen.permutation:
np.random.shuffle(phenotype)
b_cov=B_covariates(covariates,phenotype,intercept=intercept)
C=C_matrix(phenotype)
a_test=A_tests(covariates,genotype,intercept=intercept)
a_inv=A_inverse(a_cov,a_test)
N_con=a_inv.shape[1] - 1
DF=(phenotype.shape[0] - a_inv.shape[1])
b4=B4(phenotype,genotype)
t_stat, SE=HASE(b4, a_inv, b_cov, C, N_con, DF)
print('Read {}, processed {}, total {}'.format(SNP[0],SNP[1],SNP[2] ))
Analyser.t_stat=t_stat
Analyser.SE=SE
if mapper is not None and mapper.cluster == 'y':
Analyser.cluster=True
Analyser.chunk=ch
Analyser.node=mapper.node[1]
if phen.permutation:
Analyser.permutation=True
Analyser.save_result( phen.folder._data.names[phen.folder._data.start:phen.folder._data.finish])
t_stat=None
Analyser.t_stat=None
del b4
del C
del b_cov
del a_inv
del a_test
del t_stat
gc.collect()
if Analyser.cluster:
np.save(os.path.join(Analyser.out,str(Analyser.node)+'_node_RSID.npy'),Analyser.rsid_dic)
def partial_derivatives(save_path=None,
COV=None,
PHEN=None,
GEN=None,
MAP=None,
MAF=None,
R2=None,
B4_flag=False,
study_name=None,
intercept=True):
row_index, ids = study_indexes(phenotype=PHEN.folder._data,
genotype=GEN.folder._data,
covariates=COV.folder._data)
metadata = {}
#TODO (mid) add parameter to compute PD only for new phenotypes or cov
metadata['id'] = ids
metadata['MAF'] = []
metadata['filter'] = []
metadata['names'] = [] #TODO (low) change to cov_names
metadata['phenotype'] = []
b_cov = []
C = []
a_test = []
b4 = []
covariates = COV.get_next(index=row_index[2])
if MAP.cluster == 'n' or MAP.node[1] == 1:
if intercept:
metadata['names'].append(study_name + '_intercept')
metadata['names'] = metadata['names'] + [
study_name + '_' + i for i in COV.folder._data.get_names()
]
a_cov = A_covariates(covariates, intercept=intercept)
np.save(os.path.join(save_path, study_name + '_a_cov.npy'), a_cov)
with Timer() as t_phen:
while True:
phenotype = PHEN.get_next(index=row_index[1])
if isinstance(phenotype, type(None)):
b_cov = np.concatenate(b_cov, axis=1)
C = np.concatenate(C, axis=0)
np.save(os.path.join(save_path, study_name + '_b_cov.npy'),
b_cov)
np.save(os.path.join(save_path, study_name + '_C.npy'), C)
break
# Moved to commented line below the outside of this while loop as the names
# are not sliced like the 'PHEN.get_next(...)' does for phenotype.
# This solves an exception being thrown when a larger number of samples is used. (> 1000)
# metadata['phenotype'] = metadata['phenotype'] + list(PHEN.folder._data.get_names())
b_cov.append(
B_covariates(covariates, phenotype, intercept=intercept))
C.append(C_matrix(phenotype))
# Moved from the while loop above to fix bug. See ^^^ for more.
metadata['phenotype'] = metadata['phenotype'] + list(
PHEN.folder._data.get_names())
print('Time to PD phenotype {} is {} s'.format(
np.array(C).shape, t_phen.secs))
if MAP.cluster == 'y':
f_max = np.max([int(f.split('_')[0]) for f in GEN.folder.files])
files2read = [
'{}_{}.h5'.format(i, study_name)
for i in np.array_split(range(f_max +
1), MAP.node[0])[MAP.node[1] - 1]
][::-1]
filesdone = []
for i in range(MAP.node[1] - 1):
filesdone = filesdone + [
'{}_{}.h5'.format(i, study_name)
for i in np.array_split(range(f_max + 1), MAP.node[0])[i]
]
N_snps_read = 0
for f in filesdone:
file = os.path.join(GEN.folder.path, 'genotype', f)
N_snps_read += GEN.folder.get_info(file)['shape'][0]
else:
N_snps_read = 0
while True:
with Timer() as t_gen:
if MAP.cluster == 'y':
if len(files2read) != 0:
file = os.path.join(GEN.folder.path, 'genotype',
files2read.pop())
genotype = GEN.folder.read(file)
else:
genotype = None
else:
genotype = GEN.get_next()
if isinstance(genotype, type(None)):
if MAP.cluster == 'y':
np.save(
os.path.join(
save_path, 'node_{}_'.format(MAP.node[1]) +
study_name + '_a_test.npy'),
np.concatenate(a_test).astype(np.float64))
np.save(
os.path.join(
save_path, 'node_{}_'.format(MAP.node[1]) +
study_name + '_metadata.npy'), metadata)
if B4_flag:
b4 = np.concatenate(b4, axis=0)
np.save(
os.path.join(
save_path, 'node_{}_'.format(MAP.node[1]) +
study_name + '_b4.npy'), b4.astype(np.float64))
if MAP.node[1] == MAP.node[0]:
merge_PD(save_path, MAP.node[0], study_name)
else:
np.save(
os.path.join(save_path, study_name + '_a_test.npy'),
np.concatenate(a_test))
np.save(
os.path.join(save_path, study_name + '_metadata.npy'),
metadata)
if B4_flag:
b4 = np.concatenate(b4, axis=0)
np.save(
os.path.join(save_path, study_name + '_b4.npy'),
b4)
break
flip = MAP.flip[GEN.folder.name][N_snps_read:N_snps_read +
genotype.shape[0]]
N_snps_read += genotype.shape[0]
flip_index = (flip == -1)
genotype = np.apply_along_axis(
lambda x: flip * (x - 2 * flip_index), 0, genotype)
genotype = genotype[:, row_index[0]]
maf = np.mean(genotype, axis=1) / 2
metadata['MAF'] = metadata['MAF'] + list(maf)
#TODO (low) add interaction
a_test.append(A_tests(covariates, genotype, intercept=intercept))
if B4_flag:
#works only when all phenotypes in one chunk, if not, do not use this option!
#it would use to much disk space anyway
if len([f for f in PHEN.folder.files if f != 'info_dic.npy'
]) > 1:
print 'pd_full flag disabled!'
B4_flag = False
continue
PHEN.folder.processed = 0
phenotype = PHEN.get_next(index=row_index[1])
b4.append(B4(phenotype, genotype))
print('Time to PD genotype {} is {} s'.format(genotype.shape,
t_gen.secs))
mapper.genotype_names=args.study_name
mapper.chunk_size=MAPPER_CHUNK_SIZE
mapper.reference_name=args.ref_name
mapper.load_flip(args.mapper)
mapper.load(args.mapper)
phen=Reader('phenotype')
phen.start(args.phenotype[0])
gen=Reader('genotype')
gen.start(args.genotype[0], hdf5=args.hdf5, study_name=args.study_name[0], ID=False)
e=Encoder(args.out)
e.study_name=args.study_name[0]
row_index, ids = study_indexes(phenotype=phen.folder._data,genotype=gen.folder._data)
with Timer() as t:
e.matrix(len(ids),save=True)
N_snps_read=0
while True:
with Timer() as t_gen:
genotype = gen.get_next()
if isinstance(genotype, type(None)):
break
flip=mapper.flip[N_snps_read:N_snps_read+genotype.shape[0],0]
N_snps_read+=genotype.shape[0]
flip_index=(flip==-1)
genotype=np.apply_along_axis(lambda x: flip*(x-2*flip_index) ,0,genotype)
genotype=genotype[:,row_index[0]]
