This repo includes a PyTorch HuggingFace Accelerator implementation of the UCE model, to be used to embed individual anndata datasets.

pip install -r requirements.txt

To generate an embedding for a new single-cell RNA sequencing dataset in the AnnData format, use the eval_single_anndata.py script.

python eval_single_anndata.py --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 .X slot of the file should be scRNA-seq counts. The .var_names slot 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 .torch file.
• 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.

This script also makes use of additional files, which are described in the code documentation. These are downloaded automatically unless already present in the working directory.

For a sample output on the 10k pbmc dataset, run eval_single_anndata.py with default parameters.

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 eval_single_anndata.py are stored in the dir directory.

You can download processed datasets used in the papere here