Summary of Domain Adaptive and Fine-grained Anomaly Detection For Single-cell Sequencing Data and Beyond, by Kaichen Xu et al.
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
by Kaichen Xu, Yueyang Ding, Suyang Hou, Weiqiang Zhan, Nisang Chen, Jun Wang, Xiaobo Sun
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel generative framework, ACSleuth, is proposed for detecting and subtyping anomalous cells from single-cell sequencing data. This method integrates domain adaptation, anomaly detection, and fine-grained annotation to improve performance in multi-sample and multi-domain contexts. The framework utilizes reconstruction deviations as an alternative to traditional domain shifts for anomaly detection, which is theoretically analyzed and shown to be superior. Extensive benchmarks demonstrate ACSleuth’s effectiveness over state-of-the-art methods in identifying and subtyping anomalies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ACSleuth is a new way to find and categorize cells that are different from normal cells. It uses data from single-cell sequencing to identify unusual cells and group them into specific types based on their characteristics. This tool helps doctors diagnose diseases more accurately and researchers understand cell behavior better. The best part is that it can handle complex data sets with multiple samples and domains, which was a challenge for previous methods. |
Keywords
» Artificial intelligence » Anomaly detection » Domain adaptation
