School of Biomedical Sciences cordially invites you to join the following seminar:

Speaker: Professor James J. Cai, Professor, Texas A&M University, USA
Talk Title: Exploring advanced frontiers in single-cell bioinformatics: moving beyond differential expression analysis

Date: 28 May 2024 (Tuesday)
Time: 4:00 pm – 5:00 pm
Venue: Lecture Theatre 1, G/F, William M.W. Mong Block, 21 Sassoon Road
Host: Professor Joshua Ho

Biography

Prof. James Cai is a Professor of Computational Systems Biology with a joint appointment in the Veterinary Integrative Biosciences and Electrical & Computer Engineering departments at Texas A&M University. His research integrates genomics, computational statistics, and data science, employing machine learning, graph theory, and quantum computation to understand cellular behaviors. Prof. Cai has made significant contributions to the field of single-cell computational biology, including developing the easy-to-use desktop application SCGEATOOL for single-cell transcriptome analysis without programming, and advanced machine learning methods such as scTenifoldXct for predicting cell-cell interactions, scTenifoldKnk for performing virtual gene knockouts, and scTenifoldNet for constructing and comparing single-cell gene regulatory networks. He also introduced QuantumGRN, a quantum circuit model for inferring gene regulatory networks. With expertise in data science, machine learning, and quantum computing, Prof. Cai's interdisciplinary research contributes to our understanding of cellular behavior and genetic disease mechanisms, shaping future diagnostic and therapeutic strategies.

Abstract

Differential expression (DE) analysis, a cornerstone of gene function studies, often yields inconsistent results and detects genes with limited biological relevance. This challenge is particularly acute in single-cell transcriptomics, a rapidly growing field. Prof. James Cai will address the underlying limitations of DE and introduce data-driven methods to extract meaningful biological insights from single-cell data. These methods go beyond DE and include: (1) Gene regulatory network construction and comparison—this approach reveals how genes interact and identifies key regulators in different cellular contexts; (2) Virtual gene knockout analysis—this technique simulates gene inactivation to predict functional consequences and identify essential genes; and (3) Manifold learning analysis for cell-cell communication—this method uncovers communication patterns between cells, providing insights into cellular interactions. By moving beyond DE, these approaches offer a deeper understanding of gene function and cellular processes in single-cell transcriptomics data.

ALL ARE WELCOME
Should you have any enquiries, please feel free to contact Miss Crystal Chan at 3917 6830.