def find_incoming_edges(self, t):
"""
find incoming edges that build a v-structure a-->t<--b with
a) corr(a,t)
b) corr(b,t)
c) ind(a,b)
d) corr(a,b t)
input:
t : index of the gene t
"""
# incoming edges are associated with the gene of interest...
pv_genes = self.genecorr_reader.getRows([t])[0]
idx_assoc = qvalue.estimate(pv_genes) < self.thresh_corr
idx_assoc[t] = False
if not (idx_assoc).any():
return None, None
# independent of each other
_idx_assoc = np.nonzero(idx_assoc)[0]
pv_genes = self.genecorr_reader.getRows(_idx_assoc)[:, idx_assoc]
idx_vstruct = np.nonzero(idx_assoc)[0]
vstruct = pv_genes > self.thresh_ind
idx_ind = vstruct.any(axis=1)
idx_vstruct = idx_vstruct[idx_ind]
vstruct = vstruct[idx_ind][:, idx_ind]
if not (idx_vstruct).any():
return None, None
# becoming dependent once we condition on the gene under observation
Yv = self.phenoreader.getRows(idx_vstruct)
Yt = self.phenoreader.getRows([t])[0]
_, pv_cond = pcor.pcorParallel(Yv, Yt)
qv_cond = qvalue.estimate(pv_cond)
vstruct *= qv_cond < self.thresh_corr
idx_partcorr = vstruct.any(axis=0)
if not (idx_partcorr).any():
return None, None
vstruct = vstruct[idx_partcorr][:, idx_partcorr]
idx_vstruct = idx_vstruct[idx_partcorr]
return vstruct, idx_vstruct
def find_incoming_edges(self, t):
"""
find incoming edges that build a v-structure a-->t<--b with
a) corr(a,t)
b) corr(b,t)
c) ind(a,b)
d) corr(a,b t)
input:
t : index of the gene t
"""
# incoming edges are associated with the gene of interest...
pv_genes = self.genecorr_reader.getRows([t])[0]
idx_assoc = qvalue.estimate(pv_genes) < self.thresh_corr
idx_assoc[t] = False
if not (idx_assoc).any(): return None, None
# independent of each other
_idx_assoc = np.nonzero(idx_assoc)[0]
pv_genes = self.genecorr_reader.getRows(_idx_assoc)[:, idx_assoc]
idx_vstruct = np.nonzero(idx_assoc)[0]
vstruct = pv_genes > self.thresh_ind
idx_ind = vstruct.any(axis=1)
idx_vstruct = idx_vstruct[idx_ind]
vstruct = vstruct[idx_ind][:, idx_ind]
if not (idx_vstruct).any(): return None, None
# becoming dependent once we condition on the gene under observation
Yv = self.phenoreader.getRows(idx_vstruct)
Yt = self.phenoreader.getRows([t])[0]
_, pv_cond = pcor.pcorParallel(Yv, Yt)
qv_cond = qvalue.estimate(pv_cond)
vstruct *= (qv_cond < self.thresh_corr)
idx_partcorr = vstruct.any(axis=0)
if not (idx_partcorr).any(): return None, None
vstruct = vstruct[idx_partcorr][:, idx_partcorr]
idx_vstruct = idx_vstruct[idx_partcorr]
return vstruct, idx_vstruct
def find_incoming_edges(self,t):
"""
find incoming edges that build a v-structure a-->t<--b with
a) corr(a,t)
b) corr(b,t)
c) ind(a,b)
d) corr(a,b t)
input:
t : index of the gene t
f """
# incoming edges are associated with the gene of interest...
pv_genes = self.genecorr_reader.getRows([t])[0]
pv_genes[t] = np.inf # don't count self-correlation in when estimating q-values (always 0)
qv_genes = np.ones(pv_genes.shape)
qv_genes[np.isfinite(pv_genes)] = qvalue.estimate(pv_genes[np.isfinite(pv_genes)])
idx_assoc = qv_genes<self.thresh_corr
idx_assoc[t] = False
if not(idx_assoc).any(): return None,None
# independent of each other
_idx_assoc = np.nonzero(idx_assoc)[0]
pv_genes = self.genecorr_reader.getRows(_idx_assoc)[:,idx_assoc]
idx_vstruct = np.nonzero(idx_assoc)[0]
pv_genes[~np.isfinite(pv_genes)] = 0
vstruct = pv_genes > self.thresh_ind
idx_ind = vstruct.any(axis=1)
idx_vstruct = idx_vstruct[idx_ind]
vstruct = vstruct[idx_ind][:,idx_ind]
if not(idx_vstruct).any(): return None,None
# becoming dependent once we condition on the gene under observation
Yv = self.phenoreader.getRows(idx_vstruct)
Yt = self.phenoreader.getRows([t])[0]
_,pv_cond = pcor.pcorParallel(Yv,Yt)
# take nans into account
#qv_cond = qvalue.estimate(pv_cond)
np.fill_diagonal(pv_cond,np.nan)
qv_cond = np.ones(pv_cond.shape)
# upper triangular matrix
iu = np.triu_indices(pv_cond.shape[0])
pv_cond_iu = pv_cond[iu]
idx_finite = np.isfinite(pv_cond_iu)
qv_cond_iu = np.ones(pv_cond_iu.shape)
qv_cond_iu[idx_finite] = qvalue.estimate(pv_cond_iu[idx_finite])
qv_cond[iu] = qv_cond_iu
# lower triangular matrix
il = np.tril_indices(pv_cond.shape[0])
qv_cond[il] = 0
qv_cond += qv_cond.T
vstruct*= (qv_cond < self.thresh_corr)
idx_partcorr = vstruct.any(axis=0)
if not(idx_partcorr).any(): return None,None
vstruct = vstruct[idx_partcorr][:,idx_partcorr]
idx_vstruct = idx_vstruct[idx_partcorr]
return vstruct, idx_vstruct
def find_incoming_edges(self, t):
"""
find incoming edges that build a v-structure a-->t<--b with
a) corr(a,t)
b) corr(b,t)
c) ind(a,b)
d) corr(a,b t)
input:
t : index of the gene t
f """
# incoming edges are associated with the gene of interest...
pv_genes = self.genecorr_reader.getRows([t])[0]
pv_genes[t] = np.inf # don't count self-correlation in when estimating q-values (always 0)
qv_genes = np.ones(pv_genes.shape)
qv_genes[np.isfinite(pv_genes)] = qvalue.estimate(pv_genes[np.isfinite(pv_genes)])
idx_assoc = qv_genes < self.thresh_corr
idx_assoc[t] = False
if not (idx_assoc).any():
return None, None
# independent of each other
_idx_assoc = np.nonzero(idx_assoc)[0]
pv_genes = self.genecorr_reader.getRows(_idx_assoc)[:, idx_assoc]
idx_vstruct = np.nonzero(idx_assoc)[0]
pv_genes[~np.isfinite(pv_genes)] = 0
vstruct = pv_genes > self.thresh_ind
idx_ind = vstruct.any(axis=1)
idx_vstruct = idx_vstruct[idx_ind]
vstruct = vstruct[idx_ind][:, idx_ind]
if not (idx_vstruct).any():
return None, None
# becoming dependent once we condition on the gene under observation
Yv = self.phenoreader.getRows(idx_vstruct)
Yt = self.phenoreader.getRows([t])[0]
_, pv_cond = pcor.pcorParallel(Yv, Yt)
# take nans into account
# qv_cond = qvalue.estimate(pv_cond)
np.fill_diagonal(pv_cond, np.nan)
qv_cond = np.ones(pv_cond.shape)
# upper triangular matrix
iu = np.triu_indices(pv_cond.shape[0])
pv_cond_iu = pv_cond[iu]
idx_finite = np.isfinite(pv_cond_iu)
qv_cond_iu = np.ones(pv_cond_iu.shape)
qv_cond_iu[idx_finite] = qvalue.estimate(pv_cond_iu[idx_finite])
qv_cond[iu] = qv_cond_iu
# lower triangular matrix
il = np.tril_indices(pv_cond.shape[0])
qv_cond[il] = 0
qv_cond += qv_cond.T
vstruct *= qv_cond < self.thresh_corr
idx_partcorr = vstruct.any(axis=0)
if not (idx_partcorr).any():
return None, None
vstruct = vstruct[idx_partcorr][:, idx_partcorr]
idx_vstruct = idx_vstruct[idx_partcorr]
return vstruct, idx_vstruct
