def test_permutation(self):
#test permutation function
for dn in self.datasets:
D = data.load(os.path.join(self.dir_name,dn))
perm = SP.random.permutation(D['X'].shape[0])
#1. set permuattion
lmm = dlimix.CLMM()
lmm.setK(D['K'])
lmm.setSNPs(D['X'])
lmm.setCovs(D['Cov'])
lmm.setPheno(D['Y'])
if 1:
#pdb.set_trace()
perm = SP.array(perm,dtype='int32')#Windows needs int32 as long -> fix interface to accept int64 types
lmm.setPermutation(perm)
lmm.process()
pv_perm1 = lmm.getPv().ravel()
#2. do by hand
lmm = dlimix.CLMM()
lmm.setK(D['K'])
lmm.setSNPs(D['X'][perm])
lmm.setCovs(D['Cov'])
lmm.setPheno(D['Y'])
lmm.process()
pv_perm2 = lmm.getPv().ravel()
D2 = (SP.log10(pv_perm1)-SP.log10(pv_perm2))**2
RV = SP.sqrt(D2.mean())
self.assertTrue(RV<1E-6)
def test_exceptions(self):
D = data.load(os.path.join(self.dir_name,self.datasets[0]))
lmm = dlimix.CLMM()
N = 100
K = SP.eye(N)
X = SP.randn(N,100)
Y = SP.randn(N+1,1)
Cov = SP.randn(N,1)
lmm.setK(K)
lmm.setSNPs(X)
lmm.setCovs(Cov)
lmm.setPheno(Y)
try:
lmm.process()
except Exception,e:
self.assertTrue(1==1)
pass
def _getH2singleTrait(self, K, verbose=None):
"""
Internal function for parameter initialization
estimate variance components and fixed effect using a linear mixed model with an intercept and 2 random effects (one is noise)
Args:
K: covariance matrix of the non-noise random effect term
"""
verbose = dlimix.getVerbose(verbose)
# Fit single trait model
varg = sp.zeros(self.P)
varn = sp.zeros(self.P)
fixed = sp.zeros((1, self.P))
for p in range(self.P):
y = self.Y[:, p:p + 1]
# check if some sull value
I = sp.isnan(y[:, 0])
if I.sum() > 0:
y = y[~I, :]
_K = K[~I, :][:, ~I]
else:
_K = copy.copy(K)
lmm = dlimix.CLMM()
lmm.setK(_K)
lmm.setSNPs(sp.ones((y.shape[0], 1)))
lmm.setPheno(y)
lmm.setCovs(sp.zeros((y.shape[0], 1)))
lmm.setVarcompApprox0(-20, 20, 1000)
lmm.process()
delta = sp.exp(lmm.getLdelta0()[0, 0])
Vtot = sp.exp(lmm.getLSigma()[0, 0])
varg[p] = Vtot
varn[p] = delta * Vtot
fixed[:, p] = lmm.getBetaSNP()
if verbose: print p
sth = {}
sth['varg'] = varg
sth['varn'] = varn
sth['fixed'] = fixed
return sth
def test_lmm1(self):
"""basic test, comapring pv"""
for dn in self.datasets:
D = data.load(os.path.join(self.dir_name,dn))
lmm = dlimix.CLMM()
lmm.setK(D['K'])
lmm.setSNPs(D['X'])
lmm.setCovs(D['Cov'])
lmm.setPheno(D['Y'])
lmm.process()
pv = lmm.getPv().ravel()
BetaSte = lmm.getBetaSNPste().ravel()
Beta = lmm.getBetaSNP()
D2pv= ((SP.log10(pv)-SP.log10(D['pv']))**2)
# D2Beta= (Beta-D['Beta'])**2
# D2BetaSte = (BetaSte-D['BetaSte'])**2
RV = SP.sqrt(D2pv.mean())<1E-6
# RV = RV & (D2Beta.mean()<1E-6)
# RV = RV & (D2BetaSte.mean()<1E-6)
self.assertTrue(RV)
def test_lmm1(self):
"""basic test, comapring pv"""
for dn in self.datasets:
D = data.load(os.path.join(self.dir_name, dn))
#make vllarg X. This needs to be changed later
NL = 1000
self.NL = NL
X = SP.tile(D['X'], (1, self.NL))
lmm = dlimix.CLMM()
lmm.setK(D['K'])
lmm.setSNPs(X)
lmm.setCovs(D['Cov'])
lmm.setPheno(D['Y'])
lmm.process()
pv = lmm.getPv().ravel()
BetaSte = lmm.getBetaSNPste().ravel()
Beta = lmm.getBetaSNP()
D2pv = (SP.log10(pv) - SP.log10(SP.tile(D['pv'], self.NL))**2)
# D2Beta= (Beta-SP.tile(D['Beta'],self.NL))**2
# D2BetaSte = (BetaSte-SP.tile(D['BetaSte'],self.NL))**2
RV = SP.sqrt(D2pv.mean()) < 1E-6
# RV = RV & (D2Beta.mean()<1E-6)
# RV = RV & (D2BetaSte.mean()<1E-6)
self.assertTrue(RV)