scmidas 0.1.19

A torch-based integration method for single-cell multi-omic data.

MIDAS

MIDAS turns raw mosaic single-cell multimodal data into imputed, batch-corrected matrices and disentangled latent representations.

Documentation: scmidas.readthedocs.io

Key features

• Mosaic integration — handle datasets where different batches measure different modality combinations (e.g. some batches have RNA + ATAC, others only RNA).
• Multi-modal support — RNA, ADT, ATAC out of the box; configurable for additional modalities.
• Imputation — fill in missing modalities with model-derived values.
• Batch correction — remove technical variation across batches.
• Knowledge transfer — fine-tune a pre-trained reference model on a query dataset.
• Multi-GPU training — built on PyTorch Lightning, with DDP support for mosaic data.
• TensorBoard integration — live training loss and UMAP visualisations.

Installation

conda create -n scmidas python=3.12
conda activate scmidas
pip install scmidas

Quick start

The MIDAS workflow is four calls. The snippet below is an API sketch — replace ... with your data and refer to the tutorials for runnable end-to-end examples.

from scmidas.config import load_config
from scmidas.model import MIDAS

# 1. Build a model bound to a mosaic dataset.
#    Input is either a directory of per-batch MTX matrices, or a MuData
#    object via MIDAS.configure_data_from_mdata(...).
model = MIDAS.configure_data_from_dir(load_config(), ..., transform={'atac': 'binarize'})

# 2. Train.
model.train(max_epochs=2000)

# 3. Predict — latent embeddings (z_c, z_u) and imputed counts per batch.
pred = model.predict()

# 4. (Optional) UMAP of the integrated latent space.
model.get_emb_umap()

Reproducibility

Code and data to reproduce the results in the paper live on the reproducibility branch.

Citation

If you use MIDAS in your research, please cite:

@article{he2024mosaic,
  title   = {Mosaic integration and knowledge transfer of single-cell multimodal data with {MIDAS}},
  author  = {He, Zhen and Hu, Shuofeng and Chen, Yaowen and An, Sijing
             and Zhou, Jiahao and Liu, Runyan and Shi, Junfeng and Wang, Jing
             and Dong, Guohua and Shi, Jinhui and Zhao, Jiaxin and Ou-Yang, Le
             and Zhu, Yuan and Bo, Xiaochen and Ying, Xiaomin},
  journal = {Nature Biotechnology},
  volume  = {42},
  number  = {10},
  pages   = {1594--1605},
  year    = {2024},
  doi     = {10.1038/s41587-023-02040-y},
  publisher = {Nature Publishing Group}
}

Contributing

Bug reports and feature requests: please open a GitHub issue. For code contributions, branch from main, make sure pytest tests/ passes, and open a pull request — for non-trivial changes, an issue first to discuss the design is appreciated.

License

MIT.