def define_gp(Y, Xr, Sg, Ug, type):
P = Y.shape[1]
_A = sp.eye(P)
if type in 'rank1': _Cr = limix.core.covar.LowRankCov(P, 1)
elif type == 'block': _Cr = limix.core.covar.FixedCov(sp.ones((P, P)))
elif type == 'full': _Cr = limix.core.covar.FreeFormCov(P)
elif type == 'null': pass
else: print('poppo')
_Cn = limix.core.covar.FreeFormCov(P)
_Cg = limix.core.covar.FreeFormCov(P)
if type == 'null': _gp = GP2KronSum(Y=Y, Cg=_Cg, Cn=_Cn, S_R=Sg, U_R=Ug)
else: _gp = GP3KronSumLR(Y=Y, G=Xr, Cr=_Cr, Cg=_Cg, Cn=_Cn, S_R=Sg, U_R=Ug)
return _gp
def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
Y = sp.randn(N, P)
# define row caoriance
f = 10
G = 1. * (sp.rand(N, f) < 0.2)
X = 1. * (sp.rand(N, f) < 0.2)
R = covar_rescale(sp.dot(X, X.T))
R += 1e-4 * sp.eye(N)
# define col covariances
Cg = FreeFormCov(P)
self._Cg = Cg
Cn = FreeFormCov(P)
Cg.setCovariance(0.5 * sp.cov(Y.T))
Cn.setCovariance(0.5 * sp.cov(Y.T))
# define gp
self.gp = GP3KronSumLR(Y=Y, Cg=Cg, Cn=Cn, R=R, G=G, rank=1)
from limix.core.gp import GP3KronSumLR
from limix.core.gp import GP
from limix.utils.preprocess import covar_rescale
import time
import copy
import pdb
if __name__ == '__main__':
# define region and bg terms
N = 500
f = 10
P = 3
G = 1. * (sp.rand(N, f) < 0.2)
X = 1. * (sp.rand(N, f) < 0.2)
R = covar_rescale(sp.dot(X, X.T))
R += 1e-4 * sp.eye(N)
# define col covariances
Cg = FreeFormCov(P)
Cn = FreeFormCov(P)
Cg.setRandomParams()
Cn.setRandomParams()
# define pheno
Y = sp.randn(N, P)
# define GP and optimize
gp = GP3KronSumLR(Y=Y, Cg=Cg, Cn=Cn, R=R, G=G, rank=1)
gp.optimize()
def __init__(self,
Y=None,
R=None,
S_R=None,
U_R=None,
traitID=None,
F=None,
rank=1):
"""
Args:
Y: [N, P] phenotype matrix
F: [N, K] matrix of fixed effect design.
K is the number of per-trait covariates.
R: [N, N] genetic relatedness matrix between individuals.
In alternative to R, S_R and U_R can be provided.
If not specified a model without relatedness component is considered.
S_R: N vector of eigenvalues of R
U_R: [N, N] eigenvector matrix of R
traiID: P vector of the IDs of the phenotypes to analyze (optional)
rank: rank of the trait covariance matrix of the variance component to be tested (default is 1)
"""
# data
noneNone = S_R is not None and U_R is not None
self.bgRE = R is not None or noneNone
# fixed effect
msg = 'The current implementation of the full rank mtSet'
msg += ' does not support covariates.'
msg += ' We reccommend to regress out covariates and'
msg += ' subsequently quantile normalize the phenotypes'
msg += ' to a normal distribution prior to use mtSet.'
msg += ' This can be done within the LIMIX framework using'
msg += ' the methods limix.utils.preprocess.regressOut and'
msg += ' limix.utils.preprocess.gaussianize'
assert not (F is not None and self.bgRE), msg
if F is not None:
F = remove_dependent_cols(F)
A = sp.eye(Y.shape[1])
else:
A = None
#traitID
if traitID is None:
traitID = sp.array(['trait %d' % p for p in range(Y.shape[1])])
self.setTraitID(traitID)
#init covariance matrices and gp
Cg = FreeFormCov(Y.shape[1])
Cn = FreeFormCov(Y.shape[1])
G = 1. * (sp.rand(Y.shape[0], 1) < 0.2)
if self.bgRE:
self._gp = GP3KronSumLR(Y=Y,
Cg=Cg,
Cn=Cn,
R=R,
S_R=S_R,
U_R=U_R,
G=G,
rank=1)
else:
self._gp = GP2KronSumLR(Y=Y, Cn=Cn, G=G, F=F, A=A)
# null model params
self.null = None
# calls itself for column-by-column trait analysis
self.stSet = None
self.nullST = None
self.infoOpt = None
self.infoOptST = None
pass
if not os.path.exists(out_file) or 'recalc' in sys.argv:
Ns = sp.array(
[100, 150, 200, 300, 500, 800, 1200, 1600, 2000, 3000, 4000, 5000])
n_rips = 5
t = sp.zeros((Ns.shape[0], n_rips))
t0 = sp.zeros((Ns.shape[0], n_rips))
r = sp.zeros((Ns.shape[0], n_rips))
for ni, n in enumerate(Ns):
for ri in range(n_rips):
print '.. %d individuals - rip %d' % (n, ri)
print ' .. generating data'
Y, S, U, G = gen_data(N=n, P=P)
# define GPs
gp = GP3KronSumLR(Y=Y, Cg=Cg, Cn=Cn, S_R=S, U_R=U, G=G, rank=1)
gp0 = gp3ks0(mean(Y),
covariance.freeform(P),
covariance.freeform(P),
S_XX=S,
U_XX=U,
rank=1)
gp0.set_Xr(G)
gp._reset_profiler()
if 1:
gp.covar.setRandomParams()
else:
n_params = gp.covar.Cr.getNumberParams()
n_params += gp.covar.Cg.getNumberParams()
n_params += gp.covar.Cn.getNumberParams()