scCompass: An Integrated Multi‐Species scRNA‐seq Database for AI‐Ready

Abstract 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 ).