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import sys
import torch
import cooler
import click
import numpy as np
import pandas as pd
from importlib_resources import files
from torch import nn
from tqdm import tqdm
from torch.cuda.amp import autocast
from torch.utils.data import DataLoader
from sklearn.neighbors import KDTree
from polaris.model.polarisnet import polarisnet
from polaris.utils.util_data import centerPredCoolDataset
def rhoDelta(data,resol,dc,radius):
pos = data[[1, 4]].to_numpy() // resol
posTree = KDTree(pos, leaf_size=30, metric='chebyshev')
NNindexes, NNdists = posTree.query_radius(pos, r=radius, return_distance=True)
_l = []
for v in NNindexes:
_l.append(len(v))
_l=np.asarray(_l)
data = data[_l>5].reset_index(drop=True)
if data.shape[0] != 0:
pos = data[[1, 4]].to_numpy() // resol
val = data[6].to_numpy()
try:
posTree = KDTree(pos, leaf_size=30, metric='chebyshev')
NNindexes, NNdists = posTree.query_radius(pos, r=dc, return_distance=True)
except ValueError as e:
if "Found array with 0 sample(s)" in str(e):
print("#"*88,'\n#')
print("#\033[91m Error!!! The data is too sparse. Please decrease the value of: [t]\033[0m\n#")
print("#"*88,'\n')
sys.exit(1)
else:
raise
rhos = []
for i in range(len(NNindexes)):
rhos.append(np.dot(np.exp(-(NNdists[i] / dc) ** 2), val[NNindexes[i]]))
rhos = np.asarray(rhos)
_r = 100
_indexes, _dists = posTree.query_radius(pos, r=_r, return_distance=True, sort_results=True)
deltas = rhos * 0
LargerNei = rhos * 0 - 1
for i in range(len(_indexes)):
idx = np.argwhere(rhos[_indexes[i]] > rhos[_indexes[i][0]])
if idx.shape[0] == 0:
deltas[i] = _dists[i][-1] + 1
else:
LargerNei[i] = _indexes[i][idx[0]]
deltas[i] = _dists[i][idx[0]]
failed = np.argwhere(LargerNei == -1).flatten()
while len(failed) > 1 and _r < 100000:
_r = _r * 10
_indexes, _dists = posTree.query_radius(pos[failed], r=_r, return_distance=True, sort_results=True)
for i in range(len(_indexes)):
idx = np.argwhere(rhos[_indexes[i]] > rhos[_indexes[i][0]])
if idx.shape[0] == 0:
deltas[failed[i]] = _dists[i][-1] + 1
else:
LargerNei[failed[i]] = _indexes[i][idx[0]]
deltas[failed[i]] = _dists[i][idx[0]]
failed = np.argwhere(LargerNei == -1).flatten()
data['rhos']=rhos
data['deltas']=deltas
else:
data['rhos']=[]
data['deltas']=[]
return data
def pool(data,dc,resol,mindelta,t,output,radius,refine=True):
ccs = set(data.iloc[:,0])
if data.shape[0] == 0:
print("#"*88,'\n#')
print("#\033[91m Error!!! The file is empty. Please check your file.\033[0m\n#")
print("#"*88,'\n')
sys.exit(1)
data = data[data[6] > t].reset_index(drop=True)
data = data[data[4] - data[1] > 11*resol].reset_index(drop=True)
if data.shape[0] == 0:
print("#"*88,'\n#')
print("#\033[91m Error!!! The data is too sparse. Please decrease: [threshold] (minimum: 0.5).\033[0m\n#")
print("#"*88,'\n')
sys.exit(1)
data[['rhos','deltas']]=0
data=data.groupby([0]).apply(rhoDelta,resol=resol,dc=dc,radius=radius).reset_index(drop=True)
minrho=0
targetData=data.reset_index(drop=True)
loopPds=[]
chroms=tqdm(set(targetData[0]), dynamic_ncols=True)
for chrom in chroms:
chroms.desc = f"[Runing clustering on {chrom}]"
data = targetData[targetData[0]==chrom].reset_index(drop=True)
pos = data[[1, 4]].to_numpy() // resol
posTree = KDTree(pos, leaf_size=30, metric='chebyshev')
rhos = data['rhos'].to_numpy()
deltas = data['deltas'].to_numpy()
centroid = np.argwhere((rhos > minrho) & (deltas > mindelta)).flatten()
_r = 100
_indexes, _dists = posTree.query_radius(pos, r=_r, return_distance=True, sort_results=True)
LargerNei = rhos * 0 - 1
for i in range(len(_indexes)):
idx = np.argwhere(rhos[_indexes[i]] > rhos[_indexes[i][0]])
if idx.shape[0] == 0:
pass
else:
LargerNei[i] = _indexes[i][idx[0]]
failed = np.argwhere(LargerNei == -1).flatten()
while len(failed) > 1 and _r < 100000:
_r = _r * 10
_indexes, _dists = posTree.query_radius(pos[failed], r=_r, return_distance=True, sort_results=True)
for i in range(len(_indexes)):
idx = np.argwhere(rhos[_indexes[i]] > rhos[_indexes[i][0]])
if idx.shape[0] == 0:
pass
else:
LargerNei[failed[i]] = _indexes[i][idx[0]]
failed = np.argwhere(LargerNei == -1).flatten()
LargerNei = LargerNei.astype(int)
label = LargerNei * 0 - 1
for i in range(len(centroid)):
label[centroid[i]] = i
decreasingsortedIdxRhos = np.argsort(-rhos)
for i in decreasingsortedIdxRhos:
if label[i] == -1:
label[i] = label[LargerNei[i]]
val = data[6].to_numpy()
refinedLoop = []
label = label.flatten()
for l in set(label):
idx = np.argwhere(label == l).flatten()
if len(idx) > 0:
refinedLoop.append(idx[np.argmax(val[idx])])
if refine:
loopPds.append(data.loc[refinedLoop])
else:
loopPds.append(data.loc[centroid])
loopPd=pd.concat(loopPds).sort_values(6,ascending=False)
loopPd[[1, 2, 4, 5]] = loopPd[[1, 2, 4, 5]].astype(int)
loopPd[[0,1,2,3,4,5,6]].to_csv(output,sep='\t',header=False, index=False)
ccs_ = set(loopPd.iloc[:,0])
badc = ccs.difference(ccs_)
return len(loopPd),badc,ccs
@click.command()
@click.option('-b','--batchsize', type=int, default=128, help='Batch size [128]')
@click.option('-C','--cpu', type=bool, default=False, help='Use CPU [False]')
@click.option('-G','--gpu', type=str, default=None, help='Comma-separated GPU indices [auto select]')
@click.option('-c','--chrom', type=str, default=None, help='Comma separated chroms [all autosomes]')
@click.option('-nw','--workers', type=int, default=16, help='Number of cpu threads [16]')
@click.option('-t','--threshold', type=float, default=0.6, help='Loop Score Threshold [0.6]')
@click.option('-s','--sparsity', type=float, default=0.9, help='Allowed sparsity of submatrices [0.9]')
@click.option('-md','--max_distance', type=int, default=3000000, help='Max distance (bp) between contact pairs [3000000]')
@click.option('-r','--resol',type=int,default=5000,help ='Resolution [5000]')
@click.option('-dc','--distance_cutoff', type=int, default=5, help='Distance cutoff for local density calculation in terms of bin. [5]')
@click.option('-R','--radius', type=int, default=2, help='Radius threshold to remove outliers. [2]')
@click.option('-d','--mindelta', type=float, default=5, help='Min distance allowed between two loops [5]')
@click.option('--raw',type=bool,default=False,help ='Raw matrix or balanced matrix')
@click.option('-i','--input', type=str,required=True,help='Hi-C contact map path')
@click.option('-o','--output', type=str,required=True,help='.bedpe file path to save loops')
def pred(batchsize, cpu, gpu, chrom, threshold, sparsity, workers, max_distance, resol, distance_cutoff, radius, mindelta, input, output, raw, image=224):
"""Predict loops from input contact map directly
"""
print('\npolaris loop pred START :)')
center_size = image // 2
start_idx = (image - center_size) // 2
end_idx = (image + center_size) // 2
slice_obj_pred = (slice(None), slice(None), slice(start_idx, end_idx), slice(start_idx, end_idx))
slice_obj_coord = (slice(None), slice(start_idx, end_idx), slice(start_idx, end_idx))
results=[]
if cpu:
assert gpu is None, "\033[91m QAQ The CPU and GPU modes cannot be used simultaneously. Please check the command. \033[0m\n"
gpu = ['None']
device = torch.device("cpu")
print('Using CPU mode... (This may take significantly longer than using GPU mode.)')
else:
if torch.cuda.is_available():
if gpu is not None:
print("Using the specified GPU: " + gpu)
gpu=[int(i) for i in gpu.split(',')]
device = torch.device(f"cuda:{gpu[0]}")
else:
gpuIdx = torch.cuda.current_device()
device = torch.device(gpuIdx)
print("Automatically selected GPU: " + str(gpuIdx))
gpu=[gpu]
else:
device = torch.device("cpu")
gpu = ['None']
cpu = True
print('GPU is not available!')
print('Using CPU mode... (This may take significantly longer than using GPU mode.)')
coolfile = cooler.Cooler(input + '::/resolutions/' + str(resol))
modelstate = str(files('polaris').joinpath('model/sft_loop.pt'))
_modelstate = torch.load(modelstate, map_location=device.type)
parameters = _modelstate['parameters']
if chrom is None:
chrom =coolfile.chromnames
else:
chrom = chrom.split(',')
# for rmchr in ['chrMT','MT','chrM','M','Y','chrY','X','chrX','chrW','W','chrZ','Z']: # 'Y','chrY','X','chrX'
# if rmchr in chrom:
# chrom.remove(rmchr)
print(f"Analysing chroms: {chrom}")
model = polarisnet(
image_size=parameters['image_size'],
in_channels=parameters['in_channels'],
out_channels=parameters['out_channels'],
embed_dim=parameters['embed_dim'],
depths=parameters['depths'],
channels=parameters['channels'],
num_heads=parameters['num_heads'],
drop=parameters['drop'],
drop_path=parameters['drop_path'],
pos_embed=parameters['pos_embed']
).to(device)
model.load_state_dict(_modelstate['model_state_dict'])
if not cpu and len(gpu) > 1:
model = nn.DataParallel(model, device_ids=gpu)
model.eval()
print('\n********score START********')
badc=[]
chrom_ = tqdm(chrom, dynamic_ncols=True)
for _chrom in chrom_:
test_data = centerPredCoolDataset(coolfile,_chrom,max_distance_bin=max_distance//resol,w=image,step=center_size,s=sparsity,raw=raw)
test_dataloader = DataLoader(test_data, batch_size=batchsize, shuffle=False,num_workers=workers,prefetch_factor=4,pin_memory=(gpu is not None))
chrom_.desc = f"[Analyzing {_chrom} with {len(test_data)} submatrices]"
if len(test_data) == 0:
badc.append(_chrom)
with torch.no_grad():
for X in test_dataloader:
bin_i,bin_j,targetX=X
bin_i = bin_i*resol
bin_j = bin_j*resol
with autocast():
pred = torch.sigmoid(model(targetX.float().to(device)))[slice_obj_pred].flatten()
loop = torch.nonzero(pred>threshold).flatten().cpu()
prob = pred[loop].cpu().numpy().flatten().tolist()
frag1 = bin_i[slice_obj_coord].flatten().cpu().numpy()[loop].flatten().tolist()
frag2 = bin_j[slice_obj_coord].flatten().cpu().numpy()[loop].flatten().tolist()
for i in range(len(frag1)):
# if frag1[i] < frag2[i] and frag2[i]-frag1[i] > 11*resol and frag2[i]-frag1[i] < max_distance:
if frag1[i] < frag2[i] and frag2[i]-frag1[i] < max_distance:
results.append([_chrom, frag1[i], frag1[i] + resol,
_chrom, frag2[i], frag2[i] + resol,
prob[i]])
if len(badc)==len(chrom):
raise ValueError("score FAILED :(\nThe '-s' value needs to be increased for more sparse data.")
else:
print(f'********score FINISHED********')
if len(badc)>0:
print(f"路 But the size of {badc} are too small or their contact matrix are too sparse.\n路 You may need to check the data or run these chr respectively by increasing -s.")
print(f'********pool START********')
df = pd.DataFrame(results)
loopNum,badcp,ccs = pool(df,distance_cutoff,resol,mindelta,threshold,output,radius)
if len(badcp) == len(ccs):
raise ValueError("pool FAILED :(\nPlease check input and mcool file to yield scoreFile. Or use higher '-s' value for more sparse mcool data.")
else:
print(f'********pool FINISHED********')
if len(badcp) > 0:
print(f"路 But the loop score of {badcp} are too sparse.\n路 You may need to check the mcool data or re-run polaris loop score by increasing -s.")
print(f'\npolaris loop pred FINISHED :)\n{loopNum} loops saved to {output}')
if __name__ == '__main__':
pred() |