{"ID":2885595,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04747","arxiv_id":"2508.04747","title":"GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation","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. In this paper, we propose a principled inference-time paradigm for zero-shot cell type annotation (GRIT) which bridges the scalability of pre-trained foundation models with the structural robustness relied upon in human expert annotation workflows. Specifically, we enforce local consistency of the zero-shot CLIP logits over the task-specific PCA-based $k$-NN graph. 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 zero-shot cell type annotation.","short_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...","url_abs":"https://arxiv.org/abs/2508.04747","url_pdf":"https://arxiv.org/pdf/2508.04747v2","authors":"[\"Tianxiang Hu\",\"Chenyi Zhou\",\"Jiaxiang Liu\",\"Jiongxin Wang\",\"Ruizhe Chen\",\"Haoxiang Xia\",\"Gaoang Wang\",\"Jian Wu\",\"Zuozhu Liu\"]","published":"2025-08-06T07:09:46Z","proceeding":"q-bio.GN","tasks":"[\"q-bio.GN\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}