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
"""
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)
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()
rv = plink_reader.readBED(self.bfile,
useMAFencoding=True,
start=start,
nSNPs=nSNPs,
bim=bim)
Xr = rv['snps']
return Xr, region
def scan(bfile,Y,cov,null,wnds,minSnps,i0,i1,perm_i,resfile,F):
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 = MTST.MultiTraitSetTest(Y,S_XX=cov['eval'],U_XX=cov['evec'],F=F)
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 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
"""
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)
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()
rv = plink_reader.readBED(self.bfile, useMAFencoding=True, start=start, nSNPs=nSNPs, bim=bim)
Xr = rv["snps"]
return Xr, region
def scan(bfile, Y, cov, null, wnds, minSnps, i0, i1, perm_i, resfile, F):
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 = MTST.MultiTraitSetTest(Y, S_XX=cov['eval'], U_XX=cov['evec'], F=F)
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
