Transcriptome Iseeek

🚀 iSEEEK

iSEEEK是一种通用方法，通过探索基因排名来整合超大规模的单细胞转录组数据，为单细胞分析提供了有效的解决方案。

🚀 快速开始

单细胞分析简易流程

以下是一个简单的单细胞分析流程示例代码：

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()


## Data desposited in 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")

提取标记表示

以下是提取标记表示的示例代码：

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