def test_ICE_normalization_cancer():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
X = X + X.T
profile = np.ones(n)
profile[:10] = 0
profile[50:] = 2
normed_X, bias = ICE_normalization(X,
eps=1e-10,
counts_profile=profile,
output_bias=True)
assert not np.all(np.isnan(normed_X))
normed_X[np.isnan(normed_X)] = 0
mask = np.isnan(bias).flatten()
bias[np.isnan(bias)] = 1
normed_from_bias_X = X / (bias.T * bias)
normed_from_bias_X[mask] = 0
normed_from_bias_X[:, mask] = 0
assert_array_almost_equal(normed_X, normed_from_bias_X, 6)
inferred_profile = normed_X.sum(axis=0)
inferred_profile /= inferred_profile.max()
assert_array_almost_equal(inferred_profile, profile / profile.max())
# Do the same for sparse matriecs
normed_X = ICE_normalization(sparse.coo_matrix(X),
eps=1e-10,
counts_profile=profile)
def test_ICE_normalization():
n = 100
X = np.random.random((n, n))
X = X + X.T
normed_X = ICE_normalization(X, eps=1e-10, max_iter=1000000)
normed = normed_X.sum(axis=1)
assert_array_almost_equal(normed / normed.mean(), np.ones((len(X), )),
decimal=0)
def test_ICE_normalization():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
X = X + X.T
normed_X = ICE_normalization(X, eps=1e-10, max_iter=1000000)
normed = normed_X.sum(axis=1)
assert_array_almost_equal(normed / normed.mean(), np.ones((len(X), )),
decimal=0)
def gather_high_low_cool(
cooler_file='Rao2014-GM12878-DpnII-allreps-filtered.10kb.cool',
path='./data/raw/',
chromosome='22',
scale=4,
output_path='./experiment/evaluation/'):
file = os.path.join(path, cooler_file)
cool_hic = cooler.Cooler(file)
resolution = cool_hic.binsize
mat = cool_hic.matrix(balance=True).fetch('chr' + chromosome)
high_hic, idx = remove_zeros(
mat) # idx: {true, false}, len is not changed/shrinked
bool_idx = np.array(idx).flatten()
num_idx = np.array(np.where(idx)).flatten()
low_hic = sampling_hic(high_hic, scale**2, fix_seed=True)
print('high hic shape: {}.'.format(high_hic.shape), end=' ')
print('low hic shape: {}.'.format(low_hic.shape))
b = {
'chrom': ['chr{}'.format(chromosome)] * len(bool_idx),
'start': resolution * np.arange(len(bool_idx)),
'end': resolution * (np.arange(1, (len(bool_idx) + 1))),
'weight': 1.0 * bool_idx
}
bins = pd.DataFrame(data=b)
high_hic = ICE_normalization(high_hic)
low_hic = ICE_normalization(low_hic)
high_hic = triu(high_hic, format='coo')
low_hic = triu(low_hic, format='coo')
output_path = os.path.join(output_path, 'chr{}'.format(chromosome))
os.makedirs(output_path, exist_ok=True)
outfile = 'high_chr{}.cool'.format(chromosome)
print('saving file {}'.format(os.path.join(output_path, outfile)))
uri = os.path.join(output_path, outfile)
p = {
'bin1_id': num_idx[high_hic.row],
'bin2_id': num_idx[high_hic.col],
'count': high_hic.data
}
pixels = pd.DataFrame(data=p)
cooler.create_cooler(cool_uri=uri, bins=bins, pixels=pixels)
outfile = 'low_chr{}.cool'.format(chromosome)
print('saving file {}'.format(os.path.join(output_path, outfile)))
uri = os.path.join(output_path, outfile)
p = {
'bin1_id': num_idx[low_hic.row],
'bin2_id': num_idx[low_hic.col],
'count': low_hic.data
}
pixels = pd.DataFrame(data=p)
cooler.create_cooler(cool_uri=uri, bins=bins, pixels=pixels)
def test_sparse_ICE_normalization():
n = 100
X = np.random.random((n, n))
thres = (np.random.random((n, n)) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.csr_matrix(X)
true_normed_X = ICE_normalization(X, eps=1e-10, max_iter=10)
normed_X = ICE_normalization(sparse_X, eps=1e-10, max_iter=10)
assert_array_almost_equal(X, sparse_X.todense())
assert_array_almost_equal(true_normed_X, np.array(normed_X.todense()))
def test_sparse_ICE_normalization():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.csr_matrix(X)
true_normed_X = ICE_normalization(X, eps=1e-10, max_iter=10)
normed_X = ICE_normalization(sparse_X, eps=1e-10, max_iter=10)
assert_array_almost_equal(X, sparse_X.todense())
assert_array_almost_equal(true_normed_X, np.array(normed_X.todense()))
def test_sparse_ICE_normalization_triu():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.triu(X)
true_normed_X = ICE_normalization(X, eps=1e-10, max_iter=10)
true_normed_X = np.triu(true_normed_X)
X = np.triu(X)
normed_X = ICE_normalization(sparse_X, eps=1e-10, max_iter=10)
assert_array_almost_equal(X, sparse_X.todense())
# The sparse and dense version are going to be equal up to a constant
# factor
normed_X *= true_normed_X.mean() / normed_X.mean()
assert_array_almost_equal(true_normed_X, np.array(normed_X.todense()))
def test_sparse_ICE_normalization_triu():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.triu(X)
true_normed_X, true_biases = ICE_normalization(X,
eps=1e-10,
max_iter=10,
output_bias=True)
true_normed_X = np.triu(true_normed_X)
normed_X_sparse, biases_sparse = ICE_normalization(sparse_X,
eps=1e-10,
max_iter=100,
output_bias=True)
normed_X_dense, biases_dense = ICE_normalization(np.triu(X),
eps=1e-10,
max_iter=100,
output_bias=True)
# The sparse and dense version are going to be equal up to a constant
# factor
assert_array_almost_equal(normed_X_dense,
np.array(normed_X_sparse.toarray()))
normed_X_sparse *= true_normed_X.mean() / normed_X_sparse.mean()
normed_X_dense *= true_normed_X.mean() / normed_X_dense.mean()
assert_array_almost_equal(true_normed_X,
np.array(normed_X_sparse.todense()))
assert_array_almost_equal(true_normed_X, normed_X_dense)
total_counts = 5000
normed_X = ICE_normalization(sparse_X,
eps=1e-10,
total_counts=total_counts)
assert pytest.approx(normed_X.sum(), total_counts)
def test_sparse_ICE_normalization_triu():
n = 100
random_state = np.random.RandomState(seed=42)
X = random_state.randint(0, 100, size=(n, n))
thres = (random_state.rand(n, n) > 0.5).astype(bool)
X[thres] = 0
X = X + X.T
sparse_X = sparse.triu(X)
true_normed_X, true_biases = ICE_normalization(
X, eps=1e-10, max_iter=10, output_bias=True)
true_normed_X = np.triu(true_normed_X)
normed_X_sparse, biases_sparse = ICE_normalization(
sparse_X, eps=1e-10, max_iter=100,
output_bias=True)
normed_X_dense, biases_dense = ICE_normalization(
np.triu(X), eps=1e-10, max_iter=100,
output_bias=True)
# The sparse and dense version are going to be equal up to a constant
# factor
assert_array_almost_equal(normed_X_dense,
np.array(normed_X_sparse.toarray()))
normed_X_sparse *= true_normed_X.mean() / normed_X_sparse.mean()
normed_X_dense *= true_normed_X.mean() / normed_X_dense.mean()
assert_array_almost_equal(true_normed_X,
np.array(normed_X_sparse.todense()))
assert_array_almost_equal(true_normed_X, normed_X_dense)
total_counts = 5000
normed_X = ICE_normalization(sparse_X, eps=1e-10,
total_counts=total_counts)
assert_almost_equal(normed_X.sum(), total_counts)
def _prep_counts(counts_list,
lengths,
ploidy=1,
multiscale_factor=1,
normalize=True,
filter_threshold=0.04,
exclude_zeros=True,
verbose=True):
"""Copy counts, check matrix, reduce resolution, filter, and compute bias.
"""
if not isinstance(counts_list, list):
counts_list = [counts_list]
# Copy counts
counts_list = [c.copy() for c in counts_list]
# Check counts
counts_list = check_counts(counts_list,
lengths=lengths,
ploidy=ploidy,
exclude_zeros=True)
# Determine ambiguity
nbeads = lengths.sum() * ploidy
counts_dict = [('haploid' if ploidy == 1 else {
1: 'ambig',
1.5: 'pa',
2: 'ua'
}[sum(c.shape) / nbeads], c) for c in counts_list]
if len(counts_dict) != len(dict(counts_dict)):
raise ValueError(
"Can't input multiple counts matrices of the same"
" type. Inputs (%d) = %s" %
(len(counts_dict), ', '.join([x[0] for x in counts_dict])))
counts_dict = dict(counts_dict)
# Reduce resolution
lengths_lowres = lengths
for counts_type, counts in counts_dict.items():
if multiscale_factor != 1:
lengths_lowres = decrease_lengths_res(
lengths, multiscale_factor=multiscale_factor)
counts = decrease_counts_res(counts,
multiscale_factor=multiscale_factor,
lengths=lengths,
ploidy=ploidy)
counts_dict[counts_type] = counts
# Optionally filter counts
if filter_threshold is None:
filter_threshold = 0
if filter_threshold and len(counts_list) > 1:
# If there are multiple counts matrices, filter them together.
# Counts will be ambiguated for deciding which beads to remove.
# For diploid, any beads that are filtered out will be removed from both
# homologs.
if verbose:
print(
"FILTERING LOW COUNTS: manually filtering all counts together"
" by %g" % filter_threshold,
flush=True)
all_counts_ambiguated = ambiguate_counts(list(counts_dict.values()),
lengths=lengths_lowres,
ploidy=ploidy,
exclude_zeros=True)
initial_zero_beads = find_beads_to_remove(all_counts_ambiguated,
lengths_lowres.sum()).sum()
all_counts_filtered = filter_low_counts(
sparse.coo_matrix(all_counts_ambiguated),
sparsity=False,
percentage=filter_threshold +
_percent_nan_beads(all_counts_ambiguated)).tocoo()
torm = find_beads_to_remove(all_counts_filtered, lengths_lowres.sum())
if verbose:
print(' removing %d beads' %
(torm.sum() - initial_zero_beads),
flush=True)
for counts_type, counts in counts_dict.items():
if sparse.issparse(counts):
counts = counts.toarray()
counts[np.tile(torm, int(counts.shape[0] / torm.shape[0])), :] = 0.
counts[:, np.tile(torm, int(counts.shape[1] / torm.shape[0]))] = 0.
counts = sparse.coo_matrix(counts)
counts_dict[counts_type] = counts
elif filter_threshold:
# If there is just one counts matrix, filter the full, non-ambiguated
# counts matrix.
# For diploid unambiguous or partially ambigous counts, it is possible
# that a bead will be filtered out on one homolog but not another.
individual_counts_torms = np.full((lengths_lowres.sum(), ), False)
for counts_type, counts in counts_dict.items():
if verbose:
print(
'FILTERING LOW COUNTS: manually filtering %s counts by %g'
% (counts_type.upper(), filter_threshold),
flush=True)
initial_zero_beads = find_beads_to_remove(
ambiguate_counts(counts, lengths=lengths_lowres,
ploidy=ploidy), lengths_lowres.sum()).sum()
if counts_type == 'pa':
if sparse.issparse(counts):
counts = counts.toarray()
counts_filtered = np.zeros_like(counts)
homo1_upper = np.triu(counts[:min(counts.shape), :], 1)
homo1_lower = np.triu(counts[:min(counts.shape), :].T, 1)
homo2_upper = np.triu(counts[min(counts.shape):, :], 1)
homo2_lower = np.triu(counts[min(counts.shape):, :].T, 1)
counts_filtered[:min(counts.shape), :] += filter_low_counts(
sparse.coo_matrix(homo1_upper),
sparsity=False,
percentage=filter_threshold +
_percent_nan_beads(homo1_upper)).toarray()
counts_filtered[:min(counts.shape), :] += filter_low_counts(
sparse.coo_matrix(homo1_lower),
sparsity=False,
percentage=filter_threshold +
_percent_nan_beads(homo1_lower)).toarray().T
counts_filtered[min(counts.shape):, :] += filter_low_counts(
sparse.coo_matrix(homo2_upper),
sparsity=False,
percentage=filter_threshold +
_percent_nan_beads(homo2_upper)).toarray()
counts_filtered[min(counts.shape):, :] += filter_low_counts(
sparse.coo_matrix(homo2_lower),
sparsity=False,
percentage=filter_threshold +
_percent_nan_beads(homo2_lower)).toarray().T
counts = counts_filtered
else:
counts = filter_low_counts(sparse.coo_matrix(counts),
sparsity=False,
percentage=filter_threshold +
_percent_nan_beads(counts)).tocoo()
torm = find_beads_to_remove(
ambiguate_counts(counts, lengths=lengths_lowres,
ploidy=ploidy), lengths_lowres.sum())
if verbose:
print(' removing %d beads' %
(torm.sum() - initial_zero_beads),
flush=True)
individual_counts_torms = individual_counts_torms | torm
counts = sparse.coo_matrix(counts)
counts_dict[counts_type] = counts
# Optionally normalize counts
bias = None
if normalize:
if verbose:
print('COMPUTING BIAS: all counts together', flush=True)
bias = ICE_normalization(ambiguate_counts(list(counts_dict.values()),
lengths=lengths_lowres,
ploidy=ploidy,
exclude_zeros=True),
max_iter=300,
output_bias=True)[1].flatten()
# In each counts matrix, zero out counts for which bias is NaN
for counts_type, counts in counts_dict.items():
initial_zero_beads = find_beads_to_remove(
ambiguate_counts(counts, lengths=lengths_lowres,
ploidy=ploidy), lengths_lowres.sum()).sum()
if sparse.issparse(counts):
counts = counts.toarray()
counts[np.tile(np.isnan(bias), int(counts.shape[0] /
bias.shape[0])), :] = 0.
counts[:,
np.tile(np.isnan(bias), int(counts.shape[1] /
bias.shape[0]))] = 0.
counts = sparse.coo_matrix(counts)
counts_dict[counts_type] = counts
torm = find_beads_to_remove(
ambiguate_counts(counts, lengths=lengths_lowres,
ploidy=ploidy), lengths_lowres.sum())
if verbose and torm.sum() - initial_zero_beads > 0:
print(' removing %d additional beads from %s' %
(torm.sum() - initial_zero_beads, counts_type),
flush=True)
output_counts = check_counts(list(counts_dict.values()),
lengths=lengths_lowres,
ploidy=ploidy,
exclude_zeros=exclude_zeros)
return output_counts, bias
def generate_cool(input_path='./experiment/tad_boundary',
chromosomes=['22', '21', '20', '19', 'X'],
resolution=10000,
genomic_distance=2000000):
k = np.ceil(genomic_distance / resolution).astype(int)
for chro in chromosomes:
path = os.path.join(input_path, 'chr{}'.format(chro))
hicfile = 'high_chr{}.cool'.format(chro)
cool_hic = cooler.Cooler(os.path.join(path, hicfile))
mat = cool_hic.matrix(balance=True).fetch('chr' + chro)
bins = cool_hic.bins().fetch('chr' + chro)
num_idx = np.array(np.where(np.array(bins['weight']))).flatten()
high_mat = mat[num_idx, :]
high_mat = high_mat[:, num_idx]
high_mat = filter_diag_boundary(high_mat, diag_k=0, boundary_k=k)
files = [f for f in os.listdir(path) if '.npz' in f]
for file in files:
if 'high' in file or 'low' in file:
continue
print(file)
data = np.load(os.path.join(path, file), allow_pickle=True)
mat = data['hic']
namelist = file.split('_')
if len(namelist) == 3:
name = namelist[0]
else:
model = namelist[1]
win_len = namelist[3]
if model == 'hicgan':
# true_hic = np.log1p(true_hic)
mat = np.expm1(mat)
elif model == 'deephic':
minv = high_mat.min()
maxv = high_mat.max()
# true_hic = np.divide((true_hic-minv), (maxv-minv), dtype=float,out=np.zeros_like(true_hic), where=(maxv-minv) != 0)
mat = mat * (maxv - minv) + minv
mat = (mat + np.transpose(mat)) / 2
elif model == 'hicsr':
log_mat = np.log2(high_mat + 1)
# ture_hic = 2*(log_mat/np.max(log_mat)) - 1
maxv = np.max(log_mat)
log_predict_hic = (mat + 1) / 2 * maxv
mat = np.expm1(log_predict_hic)
'''elif model == 'ours':
scn, dh = scn_normalization(high_mat, max_iter=3000)
mat = scn_recover(mat, dh)'''
name = '_'.join([model, win_len])
mat = filter_diag_boundary(mat, diag_k=0, boundary_k=k)
# mat = mat[600:900, 600:900]
# print(mat)
mat = ICE_normalization(mat)
print('mat shape: {}'.format(mat.shape))
uri = os.path.join(path, '{}_chr{}.cool'.format(name, chro))
mat = triu(mat, format='coo')
# p = {'bin1_id': mat.row, 'bin2_id': mat.col, 'count': mat.data}
p = {
'bin1_id': num_idx[mat.row],
'bin2_id': num_idx[mat.col],
'count': mat.data
}
pixels = pd.DataFrame(data=p)
cooler.create_cooler(cool_uri=uri, bins=bins, pixels=pixels)
with open(os.path.join(path, 'track.ini'), 'w') as f:
f.writelines(track)
f.close()