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import random
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from torch.utils.data import Dataset
from scipy.sparse import SparseEfficiencyWarning
warnings.filterwarnings("ignore", category=SparseEfficiencyWarning)
def getLocal(mat, i, jj, w, N):
if i >= 0 and jj >= 0 and i+w <= N and jj+w <= N:
mat = mat[i:i+w,jj:jj+w].toarray()
# print(f"global: {mat.shape}")
return mat[None,...]
# pad_width = ((up, down), (left, right))
slice_pos = [[i, i+w], [jj, jj+w]]
pad_width = [[0, 0], [0, 0]]
if i < 0:
pad_width[0][0] = -i
slice_pos[0][0] = 0
if jj < 0:
pad_width[1][0] = -jj
slice_pos[1][0] = 0
if i+w > N:
pad_width[0][1] = i+w-N
slice_pos[0][1] = N
if jj+w > N:
pad_width[1][1] = jj+w-N
slice_pos[1][1] = N
_mat = mat[slice_pos[0][0]:slice_pos[0][1],slice_pos[1][0]:slice_pos[1][1]].toarray()
padded_mat = np.pad(_mat, pad_width, mode='constant', constant_values=0)
# print(f"global: {padded_mat.shape}",slice_pos, pad_width)
return padded_mat[None,...]
def upperCoo2symm(row,col,data,N=None):
# print(np.max(row),np.max(col),N)
if N:
shape=(N,N)
else:
shape=(row.max() + 1,col.max() + 1)
sparse_matrix = coo_matrix((data, (row, col)), shape=shape)
symm = sparse_matrix + sparse_matrix.T
diagVal = symm.diagonal(0)/2
symm = symm.tocsr()
symm.setdiag(diagVal)
return symm
def shuffleIFWithCount(df):
shuffled_df = df.copy()
shuffled_df[['oe', 'balanced']] = df[['oe', 'balanced']].sample(frac=1).reset_index(drop=True)
return shuffled_df
def shuffleIF(df):
if len(df)<10:
df = shuffleIFWithCount(df)
return df
min=np.min(df['bin1_id'])
max=np.max(df['bin1_id'])
distance = df['distance'].iloc[0]
bin1_id = np.random.randint(min, high=max, size=int(len(df)*1.5))
bin2_id = bin1_id + distance
pair_id = set(zip(bin1_id,bin2_id))
if len(pair_id)<len(df)-50:
bin1_id = np.random.randint(min, high=max, size=len(df))
bin2_id = bin1_id + distance
extra_pair_id = set(zip(bin1_id,bin2_id))
pair_id.update(extra_pair_id)
if len(pair_id)<len(df):
df = df.sample(len(pair_id))
pair_id = list(pair_id)
random.shuffle(pair_id)
pair_id=np.asarray(pair_id[:len(df)])
df['bin1_id']=pair_id[:,0]
df['bin2_id'] = pair_id[:,1]
return df
class centerPredCoolDataset(Dataset):
def __init__(self, coolfile, cchrom, step=224, w=224, max_distance_bin=600, decoy=False, restrictDecoy=False, s=0.9, raw=False):
'''
Args:
step (int): the step of slide window moved and also the center crop size to predict
'''
self.s=s
oeMat, decoyOeMat, N = self._processCoolFile(coolfile, cchrom, decoy=decoy, restrictDecoy=restrictDecoy, raw=raw)
self.data, self.i, self.j = self._prepare_data(oeMat, N, step, w, max_distance_bin, decoyOeMat)
del oeMat, decoyOeMat
def _prepare_data(self, oeMat, N, step, w, max_distance_bin, decoyOeMat=None):
center_crop_size = step
start_point = -(w - center_crop_size) // 2
data, i_list, j_list = [], [], []
joffset = np.repeat(np.linspace(0, w, w, endpoint=False, dtype=int)[np.newaxis, :], w, axis=0)
ioffset = np.repeat(np.linspace(0, w, w, endpoint=False, dtype=int)[:, np.newaxis], w, axis=1)
for i in range(start_point, N - w - start_point, step):
_data, _i_list, _j_list = self._process_window(oeMat, i, step, w, N, joffset, ioffset, max_distance_bin, decoyOeMat)
data.extend(_data)
i_list.extend(_i_list)
j_list.extend(_j_list)
return data, i_list, j_list
def _process_window(self, oeMat, i, step, w, N, joffset, ioffset, max_distance_bin, decoyOeMat=None):
data, i_list, j_list = [], [], []
for j in range(0, max_distance_bin, step):
jj = j + i
# if jj + w <= N and i + w <= N:
_oeMat = getLocal(oeMat, i, jj, w, N)
if np.sum(_oeMat == 0) <= (w*w*self.s):
if decoyOeMat is not None:
_decoyOeMat = getLocal(decoyOeMat, i, jj, w, N)
data.append(np.vstack((_oeMat, _decoyOeMat)))
else:
data.append(_oeMat)
i_list.append(i + ioffset)
j_list.append(jj + joffset)
return data, i_list, j_list
def _processCoolFile(self, coolfile, cchrom, decoy=False, restrictDecoy=False, raw=False):
extent = coolfile.extent(cchrom)
N = extent[1] - extent[0]
if raw:
ccdata = coolfile.matrix(balance=False, sparse=True, as_pixels=True).fetch(cchrom)
v='count'
else:
ccdata = coolfile.matrix(balance=True, sparse=True, as_pixels=True).fetch(cchrom)
v='balanced'
ccdata['bin1_id'] -= extent[0]
ccdata['bin2_id'] -= extent[0]
ccdata['distance'] = ccdata['bin2_id'] - ccdata['bin1_id']
d_means = ccdata.groupby('distance')[v].transform('mean')
ccdata[v] = ccdata[v].fillna(0)
ccdata['oe'] = ccdata[v] / d_means
ccdata['oe'] = ccdata['oe'].fillna(0)
ccdata['oe'] = ccdata['oe'] / ccdata['oe'].max()
oeMat = upperCoo2symm(ccdata['bin1_id'].ravel(), ccdata['bin2_id'].ravel(), ccdata['oe'].ravel(), N)
decoyMat = None
if decoy:
decoydata = ccdata.copy(deep=True)
np.random.seed(0)
if restrictDecoy:
decoydata = decoydata.groupby('distance').apply(shuffleIF)
else:
decoydata = decoydata.groupby('distance').apply(shuffleIFWithCount)
decoyMat = upperCoo2symm(decoydata['bin1_id'].ravel(), decoydata['bin2_id'].ravel(), decoydata['oe'].ravel(), N)
return oeMat, decoyMat, N
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.i[idx], self.j[idx], self.data[idx] |