A message passing framework for precise cell state identification with scClassify2

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.