sc-concept 0.2.4

code for scConcept

scConcept

This repository contains the python package to train and use scConcept (Single-cell contrastive cell pre-training) method for single-cell transcriptomics.

Installation

You need to have Python 3.12 or newer installed on your system. If you don't have Python installed, we recommend installing uv.

Default installation

Install the latest release of sc-concept from PyPI:

pip install sc-concept

Latest development version

To install the latest development version directly from GitHub:

pip install git+https://github.com/theislab/scConcept.git@main

Optional Flash Attention speedup

The standard installation is enough for loading pretrained models, extracting embeddings, and light adaptation. For faster inference, embedding extraction, adaptation, or large-scale training, install Flash Attention with one of the following options.

1. Recommended: cd to the project root and run ./scripts/setup_env.sh, which installs uv if needed and creates a virtual environment with the training dependencies.

2. Manual: make sure a CUDA-enabled version of PyTorch is installed. More information is available in the PyTorch installation guide. Then install Flash Attention:

MAX_JOBS=4 pip install "flash-attn>=2.7" --no-build-isolation

This can take up to an hour depending on the system specifications and whether a pre-built release of flash-attn is available for your exact versions of Python, PyTorch, and CUDA. If this takes long, we recommend using the setup script instead.

How to use

scConcept provides a simple API to load and adapt pre-trained models and extract embeddings from scRNA-seq data.

Pre-trained models

The following models are available from the scConcept Hugging Face repository. Use the value in the model_name column with concept.load_config_and_model(model_name=...).

model_name	Training corpus	Architecture	Max tokens	Species	Notes
corpus360M[multi-species]-model170M	360M cells (CellxGene 2026 + scBaseCount 2025)	170M parameters, 16 layers, 1024 hidden size, 16 heads	20,000	16 species	Largest multi-species checkpoint; best suited for cross-species applications with sufficient memory.
corpus40M-model30M	40M cells (CellxGene 2023)	30M parameters, 8 layers, 512 hidden size, 8 heads	1,000	Human	Recommended default for embedding extraction and light adaptation.

Here's a basic example:

from concept import scConcept
import scanpy as sc

# Load your single-cell data
adata = sc.read_h5ad("your_data.h5ad")

# Initialize scConcept and load a pretrained model
concept = scConcept(cache_dir='./cache/')

# Option 1: Load a model directly from HuggingFace
concept.load_config_and_model(model_name='corpus40M-model30M') 

# Option 2: Load any local model
concept.load_config_and_model(
    config='<path-to-config.yaml>',
    model_path='<path-to-model.ckpt>',
    gene_mappings_path='<path-to-gene-mappings-directory>',
)

# scConcept accepts Gene Ensemble IDs as input. You can use built-in helper methods to do the mapping if needed:
adata.var['gene_id'] = concept.map_gene_names_to_ids(
    species='hsapiens', # see concept.species for available species names
    gene_names=adata.var_names.tolist(),
)

# Extract embeddings --> adata.var['gene_id']: ENSGXXXXXXXXXXX
result = concept.extract_embeddings(adata=adata, gene_id_column='gene_id')

# Use embeddings for downstream analysis
adata.obsm['X_scConcept'] = result['cls_cell_emb']

Model adaptation

# Adapt a pre-trained model on your own data
concept.train(adata, max_steps=10000, batch_size=128) 

# Important: For multiple datasets pass them separately
concept.train([adata1, adata2, ...], max_steps=20000, batch_size=128) 

result = concept.extract_embeddings(adata=adata, gene_id_column='gene_id')
adata.obsm['X_scConcept_adapted'] = result['cls_cell_emb']

Large-scale pre-training from scratch

scConcept.train() is only for light adaptation of pretrained models or small trainings on the fly. Use train.py for distributed model pre-training from scratch over large corpus of data.

Before using train.py follow the instructions on lamindb for setting up a lamin instance.

Troubleshooting

If you encounter an error when loading a pre-trained model, try the following:

1. Remove the repository and clone the most recent version
2. Remove the cache directory (cache/ by default)
3. Run again

This will force a fresh download of the pre-trained model and should resolve most loading issues.

Citation

Bahrami, M., Tejada-Lapuerta, A., Becker, S., Hashemi G, F.S. and Theis, F.J., 2025. scConcept: Contrastive pretraining for technology-agnostic single-cell representations beyond reconstruction. bioRxiv, pp.2025-10. doi: https://doi.org/10.1101/2025.10.14.682419