时 间：2023年6月9日（周五）15：00-16：00
地 点：理科大楼A1514室
报告人：曾鹏程 上海科技大学助理教授
主持人：明静思 助理教授
摘 要：
Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets (including parallel scRNA-seq and scATACseq data profiled from the same cell) are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the celluar heterogeneity. We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data.
报告人简介：
曾鹏程，上海科技大学数学科学研究所助理教授。2018年毕业于英国纽卡斯尔大学数学与统计学院，获统计学博士学位。2019-2021年在中国香港中文大学统计系从事博士后研究工作。目前主要从事统计学、数据科学和计算生物学方面的研究工作。已在相关领域的国际权威期刊如Bioinformatics, Briefings in Bioinformatics, PloS Computational Biology和Journal of Computational and Graphical Statistics发表论文多篇。