SMS scnews item created by Miranda Luo at Fri 5 Apr 2024 0953
Type: Seminar
Distribution: World
Expiry: 9 Apr 2024
Calendar1: 8 Apr 2024 1300-1400
Auth: (jluo0722) in SMS-SAML

Statistical Bioinformatics Seminar: Yixuan Wang (CUHK)

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.  


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