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 = limix.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 = limix.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 estimateHeritabilities(self, K, verbose=False):
"""
estimate variance components and fixed effects
from a single trait model having only two terms
"""
# 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]
lmm = limix.CLMM()
lmm.setK(K)
lmm.setSNPs(SP.ones((K.shape[0], 1)))
lmm.setPheno(y)
lmm.setCovs(SP.zeros((K.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 simple_lmm(snps,
pheno,
K=None,
covs=None,
test='lrt',
NumIntervalsDelta0=100,
NumIntervalsDeltaAlt=0,
searchDelta=False):
"""
Univariate fixed effects linear mixed model test for all SNPs
Args:
snps: [N x S] SP.array of S SNPs for N individuals
pheno: [N x 1] SP.array of 1 phenotype for N individuals
K: [N x N] SP.array of LMM-covariance/kinship koefficients (optional)
If not provided, then linear regression analysis is performed
covs: [N x D] SP.array of D covariates for N individuals
test: 'lrt' for likelihood ratio test (default) or 'f' for F-test
NumIntervalsDelta0: number of steps for delta optimization on the null model (100)
NumIntervalsDeltaAlt:number of steps for delta optimization on the alt. model (0 - no optimization)
searchDelta: Carry out delta optimization on the alternative model? if yes We use NumIntervalsDeltaAlt steps
Returns:
limix LMM object
"""
t0 = time.time()
if K is None:
K = SP.eye(snps.shape[0])
lm = limix.CLMM()
lm.setK(K)
lm.setSNPs(snps)
lm.setPheno(pheno)
if covs is None:
covs = SP.ones((snps.shape[0], 1))
lm.setCovs(covs)
if test == 'lrt':
lm.setTestStatistics(0)
elif test == 'f':
lm.setTestStatistics(1)
else:
print test
raise NotImplementedError("only f or lrt are implemented")
#set number of delta grid optimizations?
lm.setNumIntervals0(NumIntervalsDelta0)
if searchDelta:
lm.setNumIntervalsAlt(NumIntervalsDeltaAlt)
else:
lm.setNumIntervalsAlt(0)
lm.process()
t1 = time.time()
print("finished GWAS testing in %.2f seconds" % (t1 - t0))
return lm
def test_exceptions(self):
D = data.load(os.path.join(self.dir_name, self.datasets[0]))
lmm = limix.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 = limix.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 = limix.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 = limix.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 = limix.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)
print "needed %.2f seconds for variance component learning" % (t1 - t0) #get optmized variance component Ke = GP['covar_E'].K() K = GP['covar'].K() #variance components are in here: params_E = GP['covar_E'].getParams() params_G = GP['covar_G'].getParams() K0 = SP.eye(K.shape[0]) #1. Main effect #covariates C = SP.ones([2 * N, 1]) lmm = limix.CLMM() lmm.setK(scale_k(K)) lmm.setSNPs(Xf) lmm.setPheno(Y) #covariates: column of ones lmm.setCovs(C) #EmmaX mode with useful default settings lmm.setEMMAX() t0 = time.time() lmm.process() t1 = time.time() pv = lmm.getPv().flatten() print "neede %.2f seconds for GWAS scan" % (t1 - t0) #comparison with standard Kpop lmm.setK(Kpopf)