  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.
