iSEEEK

一种通过探索基因排序整合超大规模单细胞转录组的通用方法

单细胞分析简易流程

import torch
import gzip
import re
from tqdm import tqdm
import numpy as np
import scanpy as sc
from torch.utils.data import DataLoader, Dataset
from transformers import PreTrainedTokenizerFast, BertForMaskedLM 

class LineDataset(Dataset):
    def __init__(self, lines):
        self.lines = lines
        self.regex = re.compile(r'\-|\.')
    def __getitem__(self, i):
        return self.regex.sub('_', self.lines[i])
    def __len__(self):
        return len(self.lines)

device = "cuda" if torch.cuda.is_available() else "cpu" 
torch.set_num_threads(2)

tokenizer = PreTrainedTokenizerFast.from_pretrained("TJMUCH/transcriptome-iseeek")
model = BertForMaskedLM.from_pretrained("TJMUCH/transcriptome-iseeek").bert
model = model.to(device)
model.eval()


## 数据存放于 https://huggingface.co/TJMUCH/transcriptome-iseeek/tree/main
lines = [s.strip().decode() for s in gzip.open("pbmc_ranking.txt.gz")]
labels = [s.strip().decode() for s in gzip.open("pbmc_label.txt.gz")]
labels = np.asarray(labels)


ds = LineDataset(lines)
dl = DataLoader(ds, batch_size=80)

features = []

for a in tqdm(dl, total=len(dl)):
    batch = tokenizer(a, max_length=128, truncation=True, 
               padding=True, return_tensors="pt")

    for k, v in batch.items():
        batch[k] = v.to(device)

    with torch.no_grad():
        out = model(**batch)

    f = out.last_hidden_state[:,0,:]
    features.extend(f.tolist())

features = np.stack(features)

adata = sc.AnnData(features)
adata.obs['celltype'] = labels
adata.obs.celltype = adata.obs.celltype.astype("category")
sc.pp.neighbors(adata, use_rep='X')
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color=['celltype','leiden'],save= "UMAP")

提取标记表征

cell_counts = len(lines)
x = np.zeros((cell_counts, len(tokenizer)), dtype=np.float16)

for a in tqdm(dl, total=len(dl)):
    batch = tokenizer(a, max_length=128, truncation=True,
               padding=True, return_tensors="pt")

    for k, v in batch.items():
        batch[k] = v.to(device)

    with torch.no_grad():
        out = model(**batch)

    eos_idxs = batch.attention_mask.sum(dim=1) - 1
    f = out.last_hidden_state
    batch_size = f.shape[0]
    input_ids = batch.input_ids

    for i in range(batch_size):
        ##genes = tokenizer.batch_decode(input_ids[i])
        token_norms = [f[i][j].norm().item() for j in range(1, eos_idxs[i])]
        idxs = input_ids[i].tolist()[1:eos_idxs[i]]
        x[counter, idxs] = token_norms
        counter = counter + 1