scCompass: An Integrated Multi‐Species scRNA‐seq Database for AI‐Ready
Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here scCompass is proposed, which provides a comprehensive resource designed to build large-scale, multi-species, and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from nearly 105 million single cells across 13 species. Using this extensive dataset, it is able to identify stable expression genes (SEGs) and organ-specific expression genes (OSGs) in humans and mice. Different scalable datasets are provided that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art single-cell foundation models. In summary, scCompass is highly efficient and scalable database for AI-ready, which combined with user-friendly data sharing, visualization, and online analysis, greatly simplifies data access and exploitation for researchers in single-cell biology (http://www.bdbe.cn/kun).
Authors
- Pengjiang Li (ORCID: https://orcid.org/0000-0003-1115-0664)
- Ping Xu (ORCID: https://orcid.org/0000-0002-8368-801X)
- Shubai Chen
- Qingqing Long (ORCID: https://orcid.org/0009-0005-7265-3101)
- Xuezhi Wang (ORCID: https://orcid.org/0000-0001-5222-248X)
- Chengrui Wang (ORCID: https://orcid.org/0000-0003-1487-0595)
- Meng Xiao (ORCID: https://orcid.org/0000-0002-7879-2357)
- Guihai Feng (ORCID: https://orcid.org/0000-0002-8726-1734)
- Yana Liu (ORCID: https://orcid.org/0000-0002-7642-4219)
- Yuanchun Zhou (ORCID: https://orcid.org/0000-0003-2144-1131)
- Ge Yang (ORCID: https://orcid.org/0000-0003-2497-8460)
- G. M. Liu
- Jingxi Dong (ORCID: https://orcid.org/0009-0001-2017-584X)
- Wenhao Liu (ORCID: https://orcid.org/0009-0005-7006-9921)
- Xin Li (ORCID: https://orcid.org/0000-0002-9015-4438)
- Jiajia Wang (ORCID: https://orcid.org/0000-0002-4878-3513)
- Pengfei Wang (ORCID: https://orcid.org/0000-0003-1075-0684)
- Ran Zhang (ORCID: https://orcid.org/0000-0003-3376-6945)
- Chunyang Zhang (ORCID: https://orcid.org/0009-0004-5645-3575)
- Yiyang Zhang (ORCID: https://orcid.org/0000-0001-8763-6324)
- Yan Chen (ORCID: https://orcid.org/0000-0002-2354-230X)
- Zhilong Hu
- Yiqiang Chen (ORCID: https://orcid.org/0000-0001-6901-3000)
- Zhen Meng (ORCID: https://orcid.org/0000-0003-0379-8583)
- Wentao Cui
- Shihua Zhang (ORCID: https://orcid.org/0000-0002-3877-9166)
- Hanyu Xie (ORCID: https://orcid.org/0009-0000-2595-7317)
- Haiping Jiang (ORCID: https://orcid.org/0000-0002-4394-6176)
- Fang Chen (ORCID: https://orcid.org/0000-0001-7702-4244)
Institutions
- Northeast Agricultural University (CN)
- Chinese Academy of Sciences (CN)
- Institute of Computing Technology (CN)
- Shandong Institute of Automation (CN)
- National Center for Mathematics and Interdisciplinary Sciences (CN)
- Computer Network Information Center (CN)
- Academy of Mathematics and Systems Science (CN)
- University of Chinese Academy of Sciences (CN)
- Institute of Zoology (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1002/advs.202500870
- Citations
- 8
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 6.45
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- Institute of Zoology, Chinese Academy of Sciences
- National Key Research and Development Program of China