Speaker: Yixuan Wang (CUHK) Abstract: Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy, and user-friendly features. However, existing tools have mostly focused on general cell type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols, and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel’s performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications. About the speaker: Yixuan Wang is a second-year Ph.D. student in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, co-advised by Prof. Yu Li and Prof. Irwin King. She received her B.S. degree at the Harbin Institute of Technology in 2022. She focuses on developing innovative deep learning approaches to address computational issues in the realms of biology and healthcare, with a specific emphasis on tackling challenges related to single-cell data. She has published six papers in Nature Communications, Bioinformatics, Briefings in Bioinformatics, and RECOMB. This event will be held online. Zoom: https://uni-sydney.zoom.us/j/84087321707