Ding Wenze, Cao Yue, Fu Xiaohang, Torkel Marni, Yang Jean Yee Hwa
School of Mathematics and Statistics, Faculty of Science, University of Sydney, Sydney, NSW, 2006, Australia.
Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.
Genome Biol. 2025 Aug 19;26(1):252. doi: 10.1186/s13059-025-03722-3.
Cell annotation is crucial for downstream exploration. Although many approaches, spanning from classic statistics to large language models, have been developed, most of their focus is on distinct cell types and overlook sequential cell populations. Here, we propose an annotation method, scClassify2, to specifically focus on adjacent cell state identification. By incorporating prior biological knowledge through a novel dual-layer architecture and ordinal regression, scClassify2 achieves competitive performance compared to other state-of-the-art methods. Besides single-cell RNA-sequencing data, scClassify2 is generalizable from different platforms including subcellular spatial transcriptomics data. We also develop a web server for academic uses.
细胞注释对于下游探索至关重要。尽管已经开发了许多方法，从经典统计到大型语言模型，但它们大多专注于不同的细胞类型，而忽略了连续的细胞群体。在这里，我们提出了一种注释方法scClassify2，专门专注于相邻细胞状态识别。通过一种新颖的双层架构和有序回归纳入先验生物学知识，与其他最先进的方法相比，scClassify2取得了具有竞争力的性能。除了单细胞RNA测序数据外，scClassify2还可从包括亚细胞空间转录组学数据在内的不同平台进行推广。我们还开发了一个供学术使用的网络服务器。
