def scan(bfile,Y,cov,null,wnds,minSnps,i0,i1,perm_i,resfile,F,colCovarType_r='lowrank',rank_r=1):
if perm_i is not None:
print 'Generating permutation (permutation %d)'%perm_i
np.random.seed(perm_i)
perm = np.random.permutation(Y.shape[0])
mtSet = limix.MTSet(Y=Y, S_R=cov['eval'], U_R=cov['evec'], F=F, rank=rank_r)
mtSet.setNull(null)
bim = plink_reader.readBIM(bfile,usecols=(0,1,2,3))
fam = plink_reader.readFAM(bfile,usecols=(0,1))
print 'fitting model'
wnd_file = csv.writer(open(resfile,'wb'),delimiter='\t')
for wnd_i in range(i0,i1):
print '.. window %d - (%d, %d-%d) - %d snps'%(wnd_i,int(wnds[wnd_i,1]),int(wnds[wnd_i,2]),int(wnds[wnd_i,3]),int(wnds[wnd_i,-1]))
if int(wnds[wnd_i,-1])<minSnps:
print 'SKIPPED: number of snps lower than minSnps'
continue
#RV = bed.read(PositionRange(int(wnds[wnd_i,-2]),int(wnds[wnd_i,-1])))
RV = plink_reader.readBED(bfile, useMAFencoding=True, blocksize = 1, start = int(wnds[wnd_i,4]), nSNPs = int(wnds[wnd_i,5]), order = 'F',standardizeSNPs=False,ipos = 2,bim=bim,fam=fam)
Xr = RV['snps']
if perm_i is not None:
Xr = Xr[perm,:]
rv = mtSet.optimize(Xr)
line = np.concatenate([wnds[wnd_i,:],rv['LLR']])
wnd_file.writerow(line)
pass
def computePCsPython(out_dir,k,bfile,ffile):
""" reading in """
RV = plink_reader.readBED(bfile,useMAFencoding=True)
X = RV['snps']
""" normalizing markers """
print 'Normalizing SNPs...'
p_ref = X.mean(axis=0)/2.
X -= 2*p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2*p_ref*(1-p_ref))
hasNan = sp.any(sp.isnan(X),axis=0)
print '%d SNPs have a nan entry. Exluding them for computing the covariance matrix.'%hasNan.sum()
X = X[:,~hasNan]
""" computing prinicipal components """
U,S,Vt = ssl.svds(X,k=k)
U -= U.mean(0)
U /= U.std(0)
U = U[:,::-1]
""" saving to output """
np.savetxt(ffile, U, delimiter='\t',fmt='%.6f')
def computeCovarianceMatrixPython(out_dir, bfile, cfile, sim_type='RRM'):
print "Using python to create covariance matrix. This might be slow. We recommend using plink instead."
if sim_type is not 'RRM':
raise Exception('sim_type %s is not known' % sim_type)
""" loading data """
data = plink_reader.readBED(bfile, useMAFencoding=True)
iid = data['iid']
X = data['snps']
N = X.shape[1]
print '%d variants loaded.' % N
print '%d people loaded.' % X.shape[0]
""" normalizing markers """
print 'Normalizing SNPs...'
p_ref = X.mean(axis=0) / 2.
X -= 2 * p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2 * p_ref * (1 - p_ref))
hasNan = sp.any(sp.isnan(X), axis=0)
print '%d SNPs have a nan entry. Exluding them for computing the covariance matrix.' % hasNan.sum(
)
""" computing covariance matrix """
print 'Computing relationship matrix...'
K = sp.dot(X[:, ~hasNan], X[:, ~hasNan].T)
K /= 1. * N
print 'Relationship matrix calculation complete'
print 'Relationship matrix written to %s.cov.' % cfile
print 'IDs written to %s.cov.id.' % cfile
""" saving to output """
np.savetxt(cfile + '.cov', K, delimiter='\t', fmt='%.6f')
np.savetxt(cfile + '.cov.id', iid, delimiter=' ', fmt='%s')
def getRegion(self,size=3e4,min_nSNPs=1,chrom_i=None,pos_min=None,pos_max=None):
"""
Sample a region from the piece of genotype X, chrom, pos
minSNPnum: minimum number of SNPs contained in the region
Ichrom: restrict X to chromosome Ichrom before taking the region
cis: bool vector that marks the sorted region
region: vector that contains chrom and init and final position of the region
"""
if (self.chrom is None) or (self.pos is None):
bim = plink_reader.readBIM(self.bfile,usecols=(0,1,2,3))
chrom = SP.array(bim[:,0],dtype=int)
pos = SP.array(bim[:,3],dtype=int)
else:
chrom = self.chrom
pos = self.pos
if chrom_i is None:
n_chroms = chrom.max()
chrom_i = int(SP.ceil(SP.rand()*n_chroms))
pos = pos[chrom==chrom_i]
chrom = chrom[chrom==chrom_i]
ipos = SP.ones(len(pos),dtype=bool)
if pos_min is not None:
ipos = SP.logical_and(ipos,pos_min<pos)
if pos_max is not None:
ipos = SP.logical_and(ipos,pos<pos_max)
pos = pos[ipos]
chrom = chrom[ipos]
if size==1:
# select single SNP
idx = int(SP.ceil(pos.shape[0]*SP.rand()))
cis = SP.arange(pos.shape[0])==idx
region = SP.array([chrom_i,pos[idx],pos[idx]])
else:
while 1:
idx = int(SP.floor(pos.shape[0]*SP.rand()))
posT1 = pos[idx]
posT2 = pos[idx]+size
if posT2<=pos.max():
cis = chrom==chrom_i
cis*= (pos>posT1)*(pos<posT2)
if cis.sum()>min_nSNPs: break
region = SP.array([chrom_i,posT1,posT2])
start = SP.nonzero(cis)[0].min()
nSNPs = cis.sum()
if self.X is None:
rv = plink_reader.readBED(self.bfile,useMAFencoding=True,start = start, nSNPs = nSNPs,bim=bim)
Xr = rv['snps']
else:
Xr = self.X[:,start:start+nSnps]
return Xr, region
def scan(bfile,
Y,
cov,
null,
wnds,
minSnps,
i0,
i1,
perm_i,
resfile,
F,
colCovarType_r='lowrank',
rank_r=1):
if perm_i is not None:
print 'Generating permutation (permutation %d)' % perm_i
np.random.seed(perm_i)
perm = np.random.permutation(Y.shape[0])
mtSet = limix.MTSet(Y=Y,
S_R=cov['eval'],
U_R=cov['evec'],
F=F,
rank=rank_r)
mtSet.setNull(null)
bim = plink_reader.readBIM(bfile, usecols=(0, 1, 2, 3))
fam = plink_reader.readFAM(bfile, usecols=(0, 1))
print 'fitting model'
wnd_file = csv.writer(open(resfile, 'wb'), delimiter='\t')
for wnd_i in range(i0, i1):
print '.. window %d - (%d, %d-%d) - %d snps' % (
wnd_i, int(wnds[wnd_i, 1]), int(wnds[wnd_i, 2]), int(
wnds[wnd_i, 3]), int(wnds[wnd_i, -1]))
if int(wnds[wnd_i, -1]) < minSnps:
print 'SKIPPED: number of snps lower than minSnps'
continue
#RV = bed.read(PositionRange(int(wnds[wnd_i,-2]),int(wnds[wnd_i,-1])))
RV = plink_reader.readBED(bfile,
useMAFencoding=True,
blocksize=1,
start=int(wnds[wnd_i, 4]),
nSNPs=int(wnds[wnd_i, 5]),
order='F',
standardizeSNPs=False,
ipos=2,
bim=bim,
fam=fam)
Xr = RV['snps']
if perm_i is not None:
Xr = Xr[perm, :]
rv = mtSet.optimize(Xr)
line = np.concatenate([wnds[wnd_i, :], rv['LLR']])
wnd_file.writerow(line)
pass
def _genBgTerm_fromSNPs(self,vTot=0.5,vCommon=0.1,pCausal=0.5,plot=False):
""" generate """
if self.X is None:
print 'Reading in all SNPs. This is slow.'
rv = plink_reader.readBED(self.bfile,useMAFencoding=True)
X = rv['snps']
else:
X = self.X
S = X.shape[1]
vSpecific = vTot-vCommon
# select causal SNPs
nCausal = int(SP.floor(pCausal*S))
Ic = selectRnd(nCausal,S)
X = X[:,Ic]
# common effect
Bc = SP.dot(self.genWeights(nCausal,self.P),self.genTraitEffect())
Yc = SP.dot(X,Bc)
Yc *= SP.sqrt(vCommon/Yc.var(0).mean())
# indipendent effect
Bi = SP.randn(nCausal,self.P)
Yi = SP.dot(X,Bi)
Yi *= SP.sqrt(vSpecific/Yi.var(0).mean())
if plot:
import pylab as PL
PL.ion()
for p in range(self.P):
PL.subplot(self.P,1,p+1)
PL.plot(SP.arange(self.X.shape[1])[Ic],Bc[:,p],'o',color='y',alpha=0.05)
PL.plot(SP.arange(self.X.shape[1])[Ic],Bi[:,p],'o',color='r',alpha=0.05)
#PL.ylim(-2,2)
PL.plot([0,Ic.shape[0]],[0,0],'k')
return Yc, Yi
def computeCovarianceMatrixPython(out_dir,bfile,cfile,sim_type='RRM'):
print "Using python to create covariance matrix. This might be slow. We recommend using plink instead."
if sim_type is not 'RRM':
raise Exception('sim_type %s is not known'%sim_type)
""" loading data """
data = plink_reader.readBED(bfile,useMAFencoding=True)
iid = data['iid']
X = data['snps']
N = X.shape[1]
print '%d variants loaded.'%N
print '%d people loaded.'%X.shape[0]
""" normalizing markers """
print 'Normalizing SNPs...'
p_ref = X.mean(axis=0)/2.
X -= 2*p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2*p_ref*(1-p_ref))
hasNan = sp.any(sp.isnan(X),axis=0)
print '%d SNPs have a nan entry. Exluding them for computing the covariance matrix.'%hasNan.sum()
""" computing covariance matrix """
print 'Computing relationship matrix...'
K = sp.dot(X[:,~hasNan],X[:,~hasNan].T)
K/= 1.*N
print 'Relationship matrix calculation complete'
print 'Relationship matrix written to %s.cov.'%cfile
print 'IDs written to %s.cov.id.'%cfile
""" saving to output """
np.savetxt(cfile + '.cov', K, delimiter='\t',fmt='%.6f')
np.savetxt(cfile + '.cov.id', iid, delimiter=' ',fmt='%s')
def computePCsPython(out_dir, k, bfile, ffile):
""" reading in """
RV = plink_reader.readBED(bfile, useMAFencoding=True)
X = RV['snps']
""" normalizing markers """
print 'Normalizing SNPs...'
p_ref = X.mean(axis=0) / 2.
X -= 2 * p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2 * p_ref * (1 - p_ref))
hasNan = sp.any(sp.isnan(X), axis=0)
print '%d SNPs have a nan entry. Exluding them for computing the covariance matrix.' % hasNan.sum(
)
X = X[:, ~hasNan]
""" computing prinicipal components """
U, S, Vt = ssl.svds(X, k=k)
U -= U.mean(0)
U /= U.std(0)
U = U[:, ::-1]
""" saving to output """
np.savetxt(ffile, U, delimiter='\t', fmt='%.6f')
