Quickstart - generate single-cell embeddings with Atacformer

This quickstart guide will help you generate single-cell embeddings using Atacformer.

Installation

To install Atacformer, you need to have the geniml package installed. You can do this using pip:

pip install geniml[ml]

Test the installation by importing Atacformer in Python:

python -c "from geniml import __version__; print(__version__)"

Loading a Pre-trained Model

You can easily load a pre-trained Atacformer model from the Hugging Face Hub. For example, you can load the base model described in our paper:

from geniml.atacformer import AtacformerForCellClustering

model = AtacformerForCellClustering.from_pretrained("databio/atacformer-base-hg38")
model = model.to("cuda")  # move the model to GPU (...if available)

Tokenize your data

To generate embeddings, you need to tokenize your genomic interval data. Frequently, this data is stored in AnnData format. You can use the geniml package to tokenize your AnnData object.

import scanpy as sc

from gtars.tokenizers import Tokenizer
from geniml.tokenization import tokenize_anndata

tokenizer = Tokenizer.from_pretrained("databio/atacformer-base-hg38")
adata = sc.read_h5ad("path/to/your/anndata.h5ad")

tokens = tokenize_anndata(adata, tokenizer)

input_ids = [t["input_ids"] for t in tokens]

Alternatively, you can tokenize .fragments.tsv.gz files directly:

from tqdm import tqdm
from gtars.tokenizers import tokenize_fragment_file

tokenized_data = tokenize_fragment_file("path/to/your/fragments.tsv.gz", tokenizer)

# qc 
MIN_COUNT = 500
MAX_COUNT = 100_000

# filter down low and high count cells
tokens_filtered = [(barcode, ids) for barcode, ids in tqdm(tokens, desc="Filtering tokens", total=len(tokens)) if len(ids) >= MIN_COUNT and len(ids) <= MAX_COUNT]
tokens_filtered = [(barcode, list(set(ids))) for barcode, ids in tqdm(tokens_filtered, desc="Removing duplicates", total=len(tokens_filtered))]

input_ids = [ids for _, ids in tokens_filtered]

Generate Embeddings

Once you have your tokenized data, you can generate embeddings using the model:

cell_embeddings = model.encode_tokenized_cells(
    input_ids=input_ids,
    batch_size=32,  # adjust based on your memory capacity
)

Downstream Tasks

You can use the generated embeddings for various downstream tasks such as clustering, classification, or visualization.