Skip to main content

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

Project description

MIDAS: a deep generative model for mosaic integration and knowledge transfer of single-cell multimodal data.

MIDAS turns mosaic data into imputed and batch-corrected data to support single-cell multimodal analysis.

Read our paper at Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.

Read our documentation at https://scmidas.readthedocs.io/en/latest/.

Installation

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

or:

pip install scmidas

🔥New

  • MIDAS supports not only RNA, ADT, and ATAC data but also allows seamless integration of additional modalities with straightforward configuration.
  • Leverages PyTorch Lightning for efficient training, including advanced strategies such as distributed data parallel (DDP).
  • Integrates with TensorBoard for real-time visualization and tracking of training metrics, such as loss.

Reproducibility

Refer to https://github.com/labomics/midas/tree/reproducibility/.

Citation

If you use MIDAS in your work, please cite the midas publication as follows:

@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 others},
  journal={Nature Biotechnology},
  pages={1--12},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scmidas-0.1.3.tar.gz (31.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

scmidas-0.1.3-py3-none-any.whl (32.0 kB view details)

Uploaded Python 3

File details

Details for the file scmidas-0.1.3.tar.gz.

File metadata

  • Download URL: scmidas-0.1.3.tar.gz
  • Upload date:
  • Size: 31.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.8.8

File hashes

Hashes for scmidas-0.1.3.tar.gz
Algorithm Hash digest
SHA256 d8958200535c2feda3fa795b743c3dd4d4788cfa0544c7de2402a0c32c886102
MD5 c28d5df45fc40c2d30f2ac9b97427daf
BLAKE2b-256 16358fa92d06f951b3b48b96ed8532cb7ed5c996d103d5f446c579428dba895c

See more details on using hashes here.

File details

Details for the file scmidas-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: scmidas-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 32.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.8.8

File hashes

Hashes for scmidas-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 a84822bd85e0048ea954a637ab59c615c68fa291d762dea0ade61eb47b3d5e2e
MD5 d76db259f4592698cd4d0b317dcc1abf
BLAKE2b-256 a75fbc7f788ff4de7e47daac4a86b05e56341f679ca4887fd7c1027ce2835235

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page