{"type":"ScholarlyArticle","title":"Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond","authors":[{"name":"Kaichen Xu","slug":"kaichen-xu","given":"Kaichen","family":"Xu","affiliation":"School of Statistics and Mathematics , Zhongnan University of Economics and Law"},{"name":"Yueyang Ding","slug":"yueyang-ding","given":"Yueyang","family":"Ding","affiliation":"School of Statistics and Mathematics , Zhongnan University of Economics and Law"},{"name":"Suyang Hou","slug":"suyang-hou","given":"Suyang","family":"Hou","affiliation":"School of Information Engineering , Zhongnan University of Economics and Law"},{"name":"Weiqiang Zhan","slug":"weiqiang-zhan","given":"Weiqiang","family":"Zhan","affiliation":"School of Statistics and Mathematics , Zhongnan University of Economics and Law"},{"name":"Nisang Chen","slug":"nisang-chen","given":"Nisang","family":"Chen","affiliation":"School of Statistics and Mathematics , Zhongnan University of Economics and Law"},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":"iWudao Tech"},{"name":"Xiaobo Sun","slug":"xiaobo-sun","given":"Xiaobo","family":"Sun","affiliation":"School of Statistics and Mathematics , Zhongnan University of Economics and Law"}],"abstract":"Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportunities for this task. However, current anomaly detection methods struggle to handle domain shifts prevalent in multi-sample and multi-domain single-cell sequencing data, leading to suboptimal performance. Moreover, these methods fall short of distinguishing anomalous cells into pathologically distinct subtypes. In response, we propose ACSleuth, a novel, reconstruction deviation-guided generative framework that integrates the detection, domain adaptation, and fine-grained annotating of anomalous cells into a methodologically cohesive workflow. Notably, we present the first theoretical analysis of using reconstruction deviations output by generative models for anomaly detection in lieu of domain shifts. This analysis informs us to develop a novel and superior maximum mean discrepancy-based anomaly scorer in ACSleuth. Extensive benchmarks over various single-cell data and other types of tabular data demonstrate ACSleuth's superiority over the state-of-the-art methods in identifying and subtyping anomalies in multi-sample and multi-domain contexts. Our code is available at https://github.com/Catchxu/ACsleuth.","doi":"10.24963/ijcai.2024/677","url":"https://ijcai.org/proceedings/2024/677","pdf":"https://ijcai.org/proceedings/2024/0677.pdf","markdown":"https://ijcai.org/proceedings/2024/677.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/677","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":677,"pages":{"first":6125,"last":6133},"keywords":["Data Mining: DM: Anomaly/outlier detection","Machine Learning: ML: Applications","Multidisciplinary Topics and Applications: MTA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Aevermann et al., 2018] Brian D. 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