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e3814d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | # This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# Using HiCCUPs to compute loop statistics as extra channel.
# --------------------------------------------------------
import numpy as np
from scipy.ndimage import correlate
from scipy.stats import poisson
def donut_kernel(R1, R2):
kernel = np.ones((R1*2+1, R1*2+1))
center = (R1, R1)
kernel[center[0] - R2 : center[0]+R2+1, center[1] - R2 : center[1]+R2+1] = 0
kernel[center[0], :] = 0
kernel[:, center[1]] = 0
return kernel
def lowerleft_kernel(R1, R2):
kernel = np.ones((R1*2+1, R1*2+1))
center = (R1, R1)
kernel[center[0] - R2 : center[0]+R2+1, center[1] - R2 : center[1]+R2+1] = 0
kernel[:center[0]+1, :] = 0
kernel[:, center[1]:] = 0
return kernel
def horizontal_kernel(R1, R2):
kernel = np.zeros((3, R1*2+1))
center = (1, R1)
kernel[ : , : center[1] - R2 ] = 1
kernel[ : , center[1] + R2 + 1 : ] = 1
return kernel
def vertical_kernel(R1, R2):
kernel = np.zeros((R1*2+1, 3))
center = (R1, 1)
kernel[ : center[0] - R2, : ] = 1
kernel[ center[0] + R2 + 1 : , : ] = 1
return kernel
class HiCCUPS:
def __init__(self,
donut_size = 5,
peak_size = 2,
lambda_step = 2**(1/3),
distance_lower_bound = 0,
distance_upper_bound = 100000,
ratio_cutoff = [1.75/0.8, 1.75/0.8, 1.5/0.8, 1.5/0.8, 2/0.8],
eps = 1e-3) -> None:
self.donut_size = donut_size
self.peak_size = peak_size
self.lambda_step = lambda_step
self.distance_lower_bound = distance_lower_bound
self.distance_upper_bound = distance_upper_bound
self.ratio_cutoff = ratio_cutoff
self.eps = eps
def __call__(self, data):
full_matrix = data['hic']
full_norm = data['norm']
kernels = [donut_kernel(self.donut_size, self.peak_size),
lowerleft_kernel(self.donut_size, self.peak_size),
horizontal_kernel(self.donut_size, self.peak_size),
vertical_kernel(self.donut_size, self.peak_size)]
l = full_matrix.shape[0]
B = min(self.distance_upper_bound, l)
window_size = min(2*B, l)
expect_vector = []
for d in range(l):
expect_vector.append(np.trace(full_matrix, d)/(l-d))
upper_triangle = np.triu(np.ones((window_size, window_size)), 0)
expect = np.zeros((window_size, window_size))
for i in range(window_size):
for j in range(window_size):
if abs(i-j) < len(expect_vector):
expect[i][j] = expect_vector[abs(i-j)]
esums = []
for kernel in kernels:
esum = correlate(expect, kernel, mode='constant') + self.eps
esums.append(esum)
qvalues = np.tile(np.ones_like(full_matrix), (len(kernels), 1,1)).astype(float)
ratios = np.zeros_like(qvalues).astype(float)
for s0 in range(0, l, B):
s = min(s0, l-window_size)
matrix = full_matrix[s:s+window_size, s:s+window_size]
norm = full_norm [s:s+window_size]
norm_mat = np.outer(norm, norm)
observed = matrix * norm_mat
observed = (np.rint(observed)).astype(int)
log_lambda_step = np.log(self.lambda_step)
for kid, kernel in enumerate(kernels):
msum = correlate(matrix, kernel, mode='constant')
esum = esums[kid]
Ek = msum/esum*expect
Ek = Ek * norm_mat + self.eps
# print(observed.shape)
# print(Ek.shape)
# print(s, window_size)
ratios[kid, s:s+window_size, s:s+window_size] = observed/Ek
#lambda-chunk FDR
logEk = np.log(Ek)
bin_id = np.ceil(np.maximum(0, logEk)/log_lambda_step).astype(int)
pvalues = poisson.sf(observed, np.exp(bin_id*log_lambda_step))
max_bin = bin_id.max()+1
for id in range(max_bin):
bin_pos = np.where((bin_id == id) & (upper_triangle == 1))
p = pvalues[bin_pos]
bin = sorted(zip(p.tolist(), bin_pos[0].tolist(), bin_pos[1].tolist()))
size = len(bin)
qvalue = 1
for rank in range(len(bin), 0, -1):
pvalue, i, j = bin[rank-1]
qvalue = min(qvalue, pvalue /(rank / size))
qvalues[kid, i+s, j+s] = qvalues[kid, j+s, i+s] = qvalue
extra_ratio = np.maximum(ratios[0], ratios[1])
extra_ratio = np.expand_dims(extra_ratio, 0)
ratios = np.concatenate([ratios, extra_ratio], axis = 0)
for kid, cutoff in enumerate(self.ratio_cutoff):
ratios[kid] = np.minimum(ratios[kid], cutoff)
ratios[kid] /= cutoff
max_qvalue = np.max(qvalues, axis=0, keepdims=True)
min_ratio = np.min(ratios, axis=0, keepdims=True)
out = np.concatenate([max_qvalue, min_ratio], axis = 0)
return out
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