GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation

• URL: http://arxiv.org/abs/2508.04747v1
• Date: Wed, 06 Aug 2025 07:09:46 GMT
• Title: GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation
• Authors: Tianxiang Hu, Chenyi Zhou, Jiaxiang Liu, Jiongxin Wang, Ruizhe Chen, Haoxiang Xia, Gaoang Wang, Jian Wu, Zuozhu Liu,
• Abstract summary: Cell type annotation is a fundamental step in the analysis of single-cell RNA sequencing (scRNA-seq) data.<n>Recent advances in CLIP-style models offer a promising path toward automating cell type annotation.<n>In this paper, we propose to refine the zero-shot logits produced by LangCell through a graph-regularized optimization framework.
• Score: 15.465706196179676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cell type annotation is a fundamental step in the analysis of single-cell RNA sequencing (scRNA-seq) data. In practice, human experts often rely on the structure revealed by principal component analysis (PCA) followed by $k$-nearest neighbor ($k$-NN) graph construction to guide annotation. While effective, this process is labor-intensive and does not scale to large datasets. Recent advances in CLIP-style models offer a promising path toward automating cell type annotation. By aligning scRNA-seq profiles with natural language descriptions, models like LangCell enable zero-shot annotation. While LangCell demonstrates decent zero-shot performance, its predictions remain suboptimal, particularly in achieving consistent accuracy across all cell types. In this paper, we propose to refine the zero-shot logits produced by LangCell through a graph-regularized optimization framework. By enforcing local consistency over the task-specific PCA-based k-NN graph, our method combines the scalability of the pre-trained models with the structural robustness relied upon in expert annotation. We evaluate our approach on 14 annotated human scRNA-seq datasets from 4 distinct studies, spanning 11 organs and over 200,000 single cells. Our method consistently improves zero-shot annotation accuracy, achieving accuracy gains of up to 10%. Further analysis showcase the mechanism by which GRIT effectively propagates correct signals through the graph, pulling back mislabeled cells toward more accurate predictions. The method is training-free, model-agnostic, and serves as a simple yet effective plug-in for enhancing automated cell type annotation in practice.

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