Universal Cell Embedding model for single-cell RNA-seq data
Project description
Universal Cell Embeddings
This repo includes a PyTorch HuggingFace Accelerator implementation of the UCE model, to be used to embed individual anndata datasets.
Installation
UCE can be installed from PyPI using pip:
python -m pip install uce-model
Embedding a new dataset
To generate an embedding for a new single-cell RNA sequencing dataset in the AnnData format, use the uce-eval-single-anndata command (depending on your environment you might need to run python -m uce-eval-single-anndata, so we will write this below).
python -m uce-eval-single-anndata --adata_path {path_to_anndata} --dir {output_dir} --species {species} --model_loc {model_loc} --batch_size {batch_size}
where
adata_path: a h5ad file. The.Xslot of the file should be scRNA-seq counts. The.var_namesslot should correspond to gene names, not ENSEMBLIDs.dir: the working directory in which intermediate and final output files will be saved to skip repeated processing of the same dataset.species: the species of the dataset you are embedding.model_loc: the location of the model weights.torchfile.batch_size: the per GPU batch size. For the 33 layer model, on a 80GB GPU, you should use 25. For a 4 layer model on the same GPU, you can use 100.
For a sample output on the 10k pbmc dataset, run
python -m uce-eval-single-anndata
All necessary model files will be downloaded automatically.
Details on all the options for uce-eval-single-anndata can be found by running python -m uce-eval-single-anndata --help.
Output from uce-eval-single-anndata --help
usage: uce-eval-single-anndata [-h] [--adata_path ADATA_PATH] [--dir DIR] [--species SPECIES] [--filter FILTER]
[--skip SKIP] [--model_loc MODEL_LOC] [--batch_size BATCH_SIZE]
[--pad_length PAD_LENGTH] [--pad_token_idx PAD_TOKEN_IDX]
[--chrom_token_left_idx CHROM_TOKEN_LEFT_IDX]
[--chrom_token_right_idx CHROM_TOKEN_RIGHT_IDX] [--cls_token_idx CLS_TOKEN_IDX]
[--CHROM_TOKEN_OFFSET CHROM_TOKEN_OFFSET] [--sample_size SAMPLE_SIZE] [--CXG CXG]
[--nlayers NLAYERS] [--output_dim OUTPUT_DIM] [--d_hid D_HID] [--token_dim TOKEN_DIM]
[--multi_gpu MULTI_GPU] [--spec_chrom_csv_path SPEC_CHROM_CSV_PATH]
[--token_file TOKEN_FILE] [--protein_embeddings_dir PROTEIN_EMBEDDINGS_DIR]
[--offset_pkl_path OFFSET_PKL_PATH]
Embed a single anndata using UCE.
options:
-h, --help show this help message and exit
--adata_path ADATA_PATH
Full path to the anndata you want to embed. (default: None)
--dir DIR Working folder where all files will be saved. (default: ./)
--species SPECIES Species of the anndata. (default: human)
--filter FILTER Additional gene/cell filtering on the anndata. (default: True)
--skip SKIP Skip datasets that appear to have already been created. (default: True)
--model_loc MODEL_LOC
Location of the model. (default: None)
--batch_size BATCH_SIZE
Batch size. (default: 25)
--pad_length PAD_LENGTH
Batch size. (default: 1536)
--pad_token_idx PAD_TOKEN_IDX
PAD token index (default: 0)
--chrom_token_left_idx CHROM_TOKEN_LEFT_IDX
Chrom token left index (default: 1)
--chrom_token_right_idx CHROM_TOKEN_RIGHT_IDX
Chrom token right index (default: 2)
--cls_token_idx CLS_TOKEN_IDX
CLS token index (default: 3)
--CHROM_TOKEN_OFFSET CHROM_TOKEN_OFFSET
Offset index, tokens after this mark are chromosome identifiers (default: 143574)
--sample_size SAMPLE_SIZE
Number of genes sampled for cell sentence (default: 1024)
--CXG CXG Use CXG model. (default: True)
--nlayers NLAYERS Number of transformer layers. (default: 4)
--output_dim OUTPUT_DIM
Output dimension. (default: 1280)
--d_hid D_HID Hidden dimension. (default: 5120)
--token_dim TOKEN_DIM
Token dimension. (default: 5120)
--multi_gpu MULTI_GPU
Use multiple GPUs (default: False)
--spec_chrom_csv_path SPEC_CHROM_CSV_PATH
CSV Path for species genes to chromosomes and start locations. (default:
./model_files/species_chrom.csv)
--token_file TOKEN_FILE
Path for token embeddings. (default: ./model_files/all_tokens.torch)
--protein_embeddings_dir PROTEIN_EMBEDDINGS_DIR
Directory where protein embedding .pt files are stored. (default:
./model_files/protein_embeddings/)
--offset_pkl_path OFFSET_PKL_PATH
PKL file which contains offsets for each species. (default:
./model_files/species_offsets.pkl)
Note: This script makes use of additional files, which are described in the code documentation. These are downloaded automatically unless already present in the working directory. The script defaults to the pretrained 4-layer model. For running the pretrained 33-layer model from the paper, please download using this link and set --nlayers 33.
Output
Final evaluated AnnData: dir/{dataset_name}.h5ad. This AnnData will be
identical to the proccessed input anndata, but have UCE embeddings added in the .obsm["X_uce"] slot.
Please see documentation for information on additional output files. All
outputs from uce-eval-single-anndata are stored in the dir directory.
Data
You can download processed datasets used in the papere here
Note: These datasets were embedded using the 33 layer model. Embeddings for the 33 layer model are not compatible with embeddings from the 4 layer model.
Using the model in Python
UCE is also a Python library, and the model can be loaded as a PyTorch module.
import uce
model = uce.get_pretrained('small') # 'small' gets a 4-layer model, 'large' gets a 33-layer model
You can also set up a dataloader and a dataset to embed it from Python.
See the documentation for uce.get_processed_dataset for more information.
dataset, dataloader = uce.get_processed_dataset(batch_size=32) # Get default dataset
Citing
If you find our paper and code useful, please consider citing the preprint:
@article{rosen2023universal,
title={Universal Cell Embeddings: A Foundation Model for Cell Biology},
author={Rosen, Yanay and Roohani, Yusuf and Agrawal, Ayush and Samotorcan, Leon and Consortium, Tabula Sapiens and Quake, Stephen R and Leskovec, Jure},
journal={bioRxiv},
pages={2023--11},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
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