Example #1
0
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