def simulate_local(self):
tmp = SQExpCov(self.X)
tmp.length = self.l2
k = tmp.K()
k *= covar_rescaling_factor_efficient(k)
self.covar += k
Xstar = sp.linspace(0,2,1000)[:,sp.newaxis]
# define mean term
W = 1. * (sp.rand(N, 2) < 0.2)
mean = lin_mean(Y, W)
# define covariance matrices
sqexp = SQExpCov(X, Xstar = Xstar)
noise = FixedCov(sp.eye(N))
covar = SumCov(sqexp, noise)
# define gp
gp = GP(covar=covar,mean=mean)
# initialize params
sqexp.scale = 1e-4
sqexp.length = 1
noise.scale = Y.var()
# optimize
gp.optimize(calc_ste=True)
# predict out-of-sample
Ystar = gp.predict()
# print optimized values and standard errors
print('weights of fixed effects')
print(mean.b[0, 0], '+/-', mean.b_ste[0, 0])
print(mean.b[1, 0], '+/-', mean.b_ste[1, 0])
print('scale of sqexp')
print(sqexp.scale, '+/-', sqexp.scale_ste)
print('length of sqexp')
print(sqexp.length, '+/-', sqexp.length_ste)
print('scale of fixed')
def run_individual_model(model, expression_file, position_file, output_directory,
permute_positions=False, random_start_point=False):
rm_diag = True
if model is not 'full' and model is not 'env':
raise Exception('model not understood. Please specify a model between full and env')
# read phenotypes data
with open(expression_file, 'r') as f:
prot_tmp = f.readline()
protein_names = prot_tmp.split(' ')
protein_names[-1] = protein_names[-1][0:-1] # removing the newline sign at the end of the last protein
protein_names = np.reshape(protein_names, [len(protein_names), 1])
phenotypes = np.loadtxt(expression_file, delimiter=' ', skiprows=1)
# read position data
X = np.genfromtxt(position_file, delimiter=',')
if permute_positions:
X = X[np.random.permutation(X.shape[0]), :]
if X.shape[0] != phenotypes.shape[0]:
raise Exception('cell number inconsistent between position and epression levels ')
# define output file name
output_file = output_directory+'/inferred_parameters_' + model
if permute_positions:
output_file += '_permuted.txt'
else:
output_file += '.txt'
N_cells = phenotypes.shape[0]
parameters = np.zeros([phenotypes.shape[1], 6])
log_lik = np.zeros(phenotypes.shape[1])
for phen in range(0, phenotypes.shape[1]):
phenotype = phenotypes[:, phen]
phenotype -= phenotype.mean()
phenotype /= phenotype.std()
phenotype = np.reshape(phenotype, [N_cells, 1])
phenotypes_tmp = np.delete(phenotypes, phen, axis=1)
phenotypes_tmp = normalise(phenotypes_tmp)
Kinship = phenotypes_tmp.dot(phenotypes_tmp.transpose())
Kinship -= np.linalg.eigvalsh(Kinship).min() * np.eye(N_cells)
Kinship *= covar_rescaling_factor(Kinship)
# create different models and print the result including likelihood
# create all the covariance terms
direct_cov = FixedCov(Kinship)
# noise
noise_cov = FixedCov(np.eye(N_cells))
# local_noise
local_noise_cov = SQExpCov(X)
local_noise_cov.length = 100
local_noise_cov.act_length = False
# environment effect
environment_cov = ZKZCov(X, Kinship, rm_diag)
# mean term
mean = MeanBase(phenotype)
#######################################################################
# defining model
#######################################################################
cov = SumCov(noise_cov, local_noise_cov)
cov = SumCov(cov, environment_cov)
if random_start_point:
environment_cov.length = np.random.uniform(10, 300)
environment_cov.scale = np.random.uniform(1, 15)
else:
environment_cov.length = 200
# environment_cov.act_length = False
if model == 'full':
cov = SumCov(cov, direct_cov)
else:
direct_cov.scale = 0
# define and optimise GP
gp = GP(covar=cov, mean=mean)
try:
gp.optimize()
except:
print('optimisation', str(phen), 'failed')
continue
log_lik[phen] = gp.LML()
# rescale each terms to sample variance one
# direct cov: unnecessary as fixed covariance rescaled before optimisation
# local noise covariance
tmp = covar_rescaling_factor(local_noise_cov.K()/local_noise_cov.scale)
local_noise_cov.scale /= tmp
# env effect
tmp = covar_rescaling_factor(environment_cov.K()/environment_cov.scale**2)
environment_cov.scale = environment_cov.scale**2/tmp
parameters[phen, :] = [direct_cov.scale,
noise_cov.scale,
local_noise_cov.scale,
local_noise_cov.length,
environment_cov.scale,
environment_cov.length]
result_header = 'direct_scale' + ' ' + \
'noise_scale' + ' ' + \
'local_noise_scale' + ' ' + \
'local_noise_length' + ' ' + \
'environment_scale' + ' ' + \
'environment_length'
with open(output_file, 'w') as f:
np.savetxt(f,
np.hstack((protein_names, parameters)),
delimiter=' ',
header=result_header,
fmt='%s',
comments='')
log_lik_file = output_file + '_loglik'
with open(log_lik_file, 'w') as f:
np.savetxt(f, log_lik)
