Transcriptome Iseeek

🚀 iSEEEK

iSEEEK是一種通用方法，通過探索基因排名來整合超大規模的單細胞轉錄組數據，為單細胞分析提供了有效的解決方案。

🚀 快速開始

單細胞分析簡易流程

以下是一個簡單的單細胞分析流程示例代碼：

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")

提取標記表示

以下是提取標記表示的示例代碼：

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