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