def test_lmm_lr(G, y, Z, Kbg, Covs=None):
"""
low-rank lmm
input:
G : genotypes
y : phenotype
Z : features of low-rank matrix
Kbg : background covariance matrix
Covs : fixed effect covariates
"""
vd = varianceDecomposition.VarianceDecomposition(y)
if Covs is not None:
vd.addFixedEffect(Covs)
vd.addRandomEffect(Kbg)
Klr = utils.computeLinearKernel(Z)
vd.addRandomEffect(Klr)
vd.addRandomEffect(is_noise=True)
vd.optimize()
varComps = vd.getVarianceComps()[0]
Ktotal = varComps[0]*Kbg + varComps[1]*Klr
lm = qtl.test_lmm(G,y,covs=Covs,K=Ktotal)
pv = lm.getPv()[0]
beta = lm.getBetaSNP()[0]
var_snps = beta**2 * np.var(G,axis=0)
var_genes = np.zeros(len(beta)) + varComps[1]
var_covs = np.zeros(len(beta))
if Covs is not None: var_covs += np.dot(Covs, vd.getWeights()).var()
return pv, beta, var_snps, var_covs, var_genes
def test_lmm_lr(G, y, Z, Kbg, Covs=None):
"""
low-rank lmm
input:
G : genotypes
y : phenotype
Z : features of low-rank matrix
Kbg : background covariance matrix
Covs : fixed effect covariates
"""
vd = varianceDecomposition.VarianceDecomposition(y)
if Covs is not None:
vd.addFixedEffect(Covs)
vd.addRandomEffect(Kbg)
Klr = utils.computeLinearKernel(Z)
vd.addRandomEffect(Klr)
vd.addRandomEffect(is_noise=True)
vd.optimize()
varComps = vd.getVarianceComps()[0]
Ktotal = varComps[0] * Kbg + varComps[1] * Klr
lm = qtl.test_lmm(G, y, covs=Covs, K=Ktotal)
pv = lm.getPv()[0]
beta = lm.getBetaSNP()[0]
var_snps = beta**2 * np.var(G, axis=0)
var_genes = np.zeros(len(beta)) + varComps[1]
var_covs = np.zeros(len(beta))
if Covs is not None: var_covs += np.dot(Covs, vd.getWeights()).var()
return pv, beta, var_snps, var_covs, var_genes
def set_K(self, K=None, X=None, U=None, S=None):
"""
setting background covariance matrix
input:
K : covariance matrix [NxN]
X : genetric markers used for constructing the covariance marix [NxF].
"""
N = self.genoreader.get_ncols()
if K is not None:
assert K.shape[0] == N
assert K.shape[1] == N
self.K = K
if X is not None:
assert X.shape[0] == N
self.K = utils.computeLinearKernel(X)
if (U is None) or (S is None):
S, U = scipy.linalg.eigh(K)
self.S = S
self.U = U
def set_K(self, K=None, X=None, U=None, S=None):
"""
setting background covariance matrix
input:
K : covariance matrix [NxN]
X : genetric markers used for constructing the covariance marix [NxF].
"""
N = self.genoreader.get_ncols()
if K is not None:
assert K.shape[0] == N
assert K.shape[1] == N
self.K = K
if X is not None:
assert X.shape[0] == N
self.K = utils.computeLinearKernel(X)
if (U is None) or (S is None):
S, U = scipy.linalg.eigh(K)
self.S = S
self.U = U
