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d622761 5ed9d51 d622761 5ed9d51 d622761 5ed9d51 d622761 5ed9d51 d622761 | 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 | import pickle,os,h5py
import numpy as np
from scipy.sparse import load_npz,csr_matrix,save_npz
import torch
from sklearn import metrics
from huggingface_hub import hf_hub_download
def pad_seq_matrix(matrix, pad_len=300):
# add flanking region to each sample
paddings = np.zeros((1, 4, pad_len)).astype('int8')
dmatrix = np.concatenate((paddings, matrix[:, :, -pad_len:]), axis=0)[:-1, :, :]
umatrix = np.concatenate((matrix[:, :, :pad_len], paddings), axis=0)[1:, :, :]
return np.concatenate((dmatrix, matrix, umatrix), axis=2)
def pad_signal_matrix(matrix, pad_len=300):
paddings = np.zeros(pad_len).astype('float32')
dmatrix = np.vstack((paddings, matrix[:, -pad_len:]))[:-1, :]
umatrix = np.vstack((matrix[:, :pad_len], paddings))[1:, :]
return np.hstack((dmatrix, matrix, umatrix))
#def load_ref_genome(chr):
# ref_path = '/nfs/turbo/umms-drjieliu/usr/zzh/KGbert/3D/data/ref_genome/'
# ref_file = os.path.join(ref_path, 'chr%s.npz' % chr)
# ref_gen_data = load_npz(ref_file).toarray().reshape(4, -1, 1000).swapaxes(0, 1)
# return torch.tensor(pad_seq_matrix(ref_gen_data))
def load_ref_genome(chr):
# Construct the filename based on the repository structure.
# If the file is at the root, use:
filename = f"chr{chr}.npz"
# Download the file from the dataset repository "luosanj/epcotv2_data"
ref_file = hf_hub_download(
repo_id="luosanj/epcotv2_data",
filename=filename,
repo_type="dataset" # Specify that it's a dataset repo
)
# Load the sparse matrix, convert it to an array, reshape, and swap axes
ref_gen_data = load_npz(ref_file).toarray().reshape(4, -1, 1000).swapaxes(0, 1)
# Pad the sequence matrix as needed and return a Torch tensor
return torch.tensor(pad_seq_matrix(ref_gen_data))
def normalize_seq(x,percentile):
data=x.data.copy()
val=np.percentile(data,percentile)
minv=data.min()
x.data= np.clip(data/val,0,1)*5
return x
def load_dnase(dnase_seq,normalize=False):
if normalize:
dnase_seq=normalize_seq(dnase_seq,98)
dnase_seq = np.expand_dims(pad_signal_matrix(dnase_seq.toarray().reshape(-1, 1000)), axis=1)
return torch.tensor(dnase_seq)
def prepare_train_data(bulk_cls):
bulk_dnase_data={}
ref_data={}
chroms=[i for i in range(1,23)]
bulk_path = '../atac_bw/'
for chr in chroms:
ref_data[chr] = load_ref_genome(chr)
for cl in bulk_cls:
bulk_dnase_data[cl]={}
with open(bulk_path + '%s_atac.pickle' % cl, 'rb') as f:
bulkdnase = pickle.load(f)
for chr in chroms:
bulk_dnase_data[cl][chr] = load_dnase(bulkdnase[chr])
return bulk_dnase_data,ref_data
def prepare_train_data_1(bulk_cls):
bulk_dnase_data={}
ref_data={}
chroms=[i for i in range(1,23)]
bulk_path = '/scratch/drjieliu_root/drjieliu/zhenhaoz/ATAC-seq/bw/'
for chr in chroms:
ref_data[chr] = load_ref_genome(chr)
for cl in bulk_cls:
bulk_dnase_data[cl]={}
with open(bulk_path + '%s_atac_1.pickle' % cl, 'rb') as f:
bulkdnase = pickle.load(f)
for chr in chroms:
bulk_dnase_data[cl][chr] = load_dnase(bulkdnase[chr])
return bulk_dnase_data,ref_data
def prepare_bru(cls):
bru={}
for cl in cls:
tmp=h5py.File('/scratch/drjieliu_root/drjieliu/zhenhaoz/bru/data/%s_bru_seq_cov.h5'%cl)['targets']
tmp=np.array(tmp).astype('float32')
tmp=np.arcsinh(tmp / np.percentile(tmp[tmp > 0], 95))
tmp=np.expand_dims(tmp,-1)
tmp1 = h5py.File('/scratch/drjieliu_root/drjieliu/zhenhaoz/bru/data/%s_bruuv_seq_cov.h5' % cl)['targets']
tmp1 = np.array(tmp1).astype('float32')
tmp1 = np.arcsinh(tmp1 / np.percentile(tmp1[tmp1 > 0], 95))
tmp1 = np.expand_dims(tmp1, -1)
tmp2 = h5py.File('/scratch/drjieliu_root/drjieliu/zhenhaoz/bru/data/%s_bruchase_seq_cov.h5' % cl)['targets']
tmp2 = np.array(tmp2).astype('float32')
tmp2 = np.arcsinh(tmp2 / np.percentile(tmp2[tmp2 > 0], 95))
tmp2 = np.expand_dims(tmp2, -1)
bru[cl]=torch.tensor(np.concatenate((tmp,tmp1,tmp2),axis=-1)).float()
print(cl,bru[cl].shape)
return bru
def prepare_rna(cls):
bru={}
for cl in cls:
tmp=h5py.File('/nfs/turbo/umms-drjieliu/proj/CAGE-seq/data/%s_cage_seq_cov.h5'%cl)['targets']
tmp=np.array(tmp).astype('float32')
tmp=np.arcsinh(tmp / np.percentile(tmp[tmp > 0], 95))
tmp=np.expand_dims(tmp,-1)
tmp1 = h5py.File('/nfs/turbo/umms-drjieliu/proj/RNA-seq/data/%s_trna_seq_cov.h5' % cl)['targets']
tmp1 = np.array(tmp1).astype('float32')
tmp1 = np.arcsinh(tmp1 / np.percentile(tmp1[tmp1 > 0], 95))
tmp1 = np.expand_dims(tmp1, -1)
tmp2 = h5py.File('/nfs/turbo/umms-drjieliu/proj/RNA-seq/data/%s_prna_seq_cov.h5' % cl)['targets']
tmp2 = np.array(tmp2).astype('float32')
tmp2 = np.arcsinh(tmp2 / np.percentile(tmp2[tmp2 > 0], 95))
tmp2 = np.expand_dims(tmp2, -1)
bru[cl]=torch.tensor(np.concatenate((tmp,tmp1,tmp2),axis=-1)).float()
print(cl,bru[cl].shape)
return bru
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