def add_covariance_for_range(r):
print(r)
range_size = r[1] - r[0]
cov = np.zeros((range_size, range_size))
range_genotypes = d.get_standardized_genotypes(r, indivs=indivs)
def compute_cov_for_snp(m):
end = d.buffer_around_snp(m, bandwidth, start=r[0], end=r[1],
units=band_units)[1]
window_start = m - r[0]
window_end = end - r[0]
window = range_genotypes[:, window_start:window_end]
cov_to_snps_in_window = \
range_genotypes[:,m-r[0]].T.dot(window) / range_genotypes.shape[0]
cov_to_snps_in_window[0] /= 2 # since we're going to symmetrize later
cov[m-r[0], window_start:window_end] = cov_to_snps_in_window
map(compute_cov_for_snp, it.show_progress(range(r[0], r[1])))
# symmetrization
ranges_to_arrays[r] = cov + cov.T
# make coding of snps consistent with other dataset
flip = np.array(IntRangeSet(positions_to_flip) & IntRangeSet((r[0],r[1])),
dtype=int) - r[0] # dtype required so we can use empty array as index
ranges_to_arrays[r][flip] *= -1
ranges_to_arrays[r][:,flip] *= -1
def compute_cov_for_slice(s):
indices = IntRangeSet((s[0] if s[0] == 0 else s[0] + int(bandwidth/2),
s[1] if s[1] == d.M else s[1] - int(bandwidth/2)))
indices = indices & snpset_irs
if indices.isempty: # if there are no indices to analyze then we can move on
return
print(s)
slice_genotypes = d.get_standardized_genotypes(s, indivs=indivs)
snpset_relative_to_slice = IntRangeSet([
(x-s[0],y-s[0]) for x,y in snpset_irs.ranges()])
def compute_cov_for_snp(m):
# we just compute the numbers needed for the top trianglular half
# of the LD matrix, then we symmetrize the matrix. (commented line is old)
# start = max(0, m - int(bandwidth/2))
start = m
end = min(slice_genotypes.shape[1], m + int(bandwidth/2))
window_indices = IntRangeSet((start, end)) & snpset_relative_to_slice
window = slice_genotypes[:, window_indices]
cov_to_snps_in_window = slice_genotypes[:,m].T.dot(window) / len(indivs)
cov_to_snps_in_window[0] /= 2 # since we're going to symmetrize later
target_indices = IntRangeSet((s[0] + start, s[0] + end)) & snpset_irs
lil_cov[s[0] + m, target_indices] = cov_to_snps_in_window
map(compute_cov_for_snp,
it.show_progress([x - s[0] for x in indices]))
def main(args):
print('reading seeed snps')
seed_snps = pd.read_csv(args.seed_snps, header=None, names=['SNP'], index_col='SNP')
seed_snps['ibs_length'] = 0
seed_snps['ibd'] = 0
print('reading typed snps')
typed_snps = pd.read_csv(args.typed_snps, header=None, names=['SNP'])
print('reading genotypes')
data = Bed(args.bfile)
X = data.read().val
typed_snps_indices = np.sort(data.sid_to_index(typed_snps.SNP))
typed_snps_bp = data.col_property[typed_snps_indices,2]
print(len(seed_snps), 'snps in list')
print(data.iid_count, data.sid_count, 'are dimensions of X')
def analyze_snp(i):
# find first typed snp after query snp
snp_bp = data.col_property[i,2]
v = np.where(typed_snps_bp > snp_bp)[0]
if len(v) > 0:
typed_i = v[0]
else:
typed_i = len(typed_snps_indices)-1
n1, n2 = np.where(X[:,i] == 1)[0]
if (X[n1,typed_snps_indices[typed_i]] - X[n2, typed_snps_indices[typed_i]])**2 == 4:
return 0, 0
typed_il, typed_ir = fis.find_boundaries(
X[n1,typed_snps_indices],
X[n2,typed_snps_indices],
typed_i)
typed_ir -= 1
il = typed_snps_indices[typed_il]
ir = typed_snps_indices[typed_ir]
cM = data.col_property[ir, 1] - \
data.col_property[il, 1]
ibd = (np.mean(X[n1,il:ir] == X[n2,il:ir]) > 0.99)
return cM, int(ibd)
for (i, snp) in iter.show_progress(
it.izip(data.sid_to_index(seed_snps.index), seed_snps.index),
total=len(seed_snps)):
# total=10):
seed_snps.ix[snp, ['ibs_length', 'ibd']] = analyze_snp(i)
print(seed_snps.iloc[:100])
seed_snps.to_csv(args.outfile, sep='\t')
def compute_cov_for_slice(s):
indices = IntRangeSet(
(s[0] if s[0] == 0 else s[0] + int(bandwidth / 2),
s[1] if s[1] == d.M else s[1] - int(bandwidth / 2)))
indices = indices & snpset_irs
if indices.isempty: # if there are no indices to analyze then we can move on
return
print(s)
slice_genotypes = d.get_standardized_genotypes(s, indivs=indivs)
snpset_relative_to_slice = IntRangeSet([
(x - s[0], y - s[0]) for x, y in snpset_irs.ranges()
])
def compute_cov_for_snp(m):
# we just compute the numbers needed for the top trianglular half
# of the LD matrix, then we symmetrize the matrix. (commented line is old)
# start = max(0, m - int(bandwidth/2))
start = m
end = min(slice_genotypes.shape[1], m + int(bandwidth / 2))
window_indices = IntRangeSet(
(start, end)) & snpset_relative_to_slice
window = slice_genotypes[:, window_indices]
cov_to_snps_in_window = slice_genotypes[:, m].T.dot(
window) / len(indivs)
cov_to_snps_in_window[
0] /= 2 # since we're going to symmetrize later
target_indices = IntRangeSet(
(s[0] + start, s[0] + end)) & snpset_irs
lil_cov[s[0] + m, target_indices] = cov_to_snps_in_window
map(compute_cov_for_snp,
it.show_progress([x - s[0] for x in indices]))
