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<p>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.&#xa0;We also develop a web server for academic uses.</p>

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A message passing framework for precise cell state identification with scClassify2

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

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  • Wenze Ding,
  • Yue Cao,
  • Xiaohang Fu,
  • Marni Torkel,
  • Jean Yee Hwa Yang

摘要

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.