scCompass: An integrated cross-species scRNA-seq database for AI-ready
Wang, P.; Liu, W.; Wang, J.; Liu, Y.; Li, P.; Xu, P.; Cui, W.; Zhang, R.; Long, Q.; Hu, Z.; Fang, C.; Dong, J.; Zhang, C.; Chen, Y.; Wang, C.; Liu, G.; Xie, H.; Zhang, Y.; Xiao, M.; Chen, S.; Chen, Y.; Yang, G.; Zhang, S.; Meng, Z.; Wang, X.; Feng, G.; Li, X.; Zhou, Y.
Show 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 we propose scCompass, which provides a data quality solution to build a large-scale, cross-species and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from 13 species and nearly 105 million single cells. Using this extensive dataset, we are able to archieve stable expression genes (SEGs) and organ-specific expression genes (OSGs) in human and mouse. We provide different scalable datasets that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art (SOTA) single-cell foundataion models. In summary, the AI-readiness of scCompass, 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).
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