报告题目：SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes
时 间：2021-07-26 (星期一) 10:00 ~ 2021-07-26 (星期一) 11:00
地 点：腾讯会议：317775575
主 办：数学与统计学院
参加对象：感兴趣师生
报告摘要：Spatial transcriptomics (ST) has been emerging as a powerful technique for resolving gene expression profiles while retaining the tissue spatial information. To answer scientific questions with spatial transcriptomics, the first step is to identify cell clusters by integrating available spatial information which would expand the understanding of how cell types and states are regulated by tissues. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using hidden Markov random field. We also derive an efficient expectation-maximization (EM) algorithm based on iterative conditional mode (ICM) for SC-MEB. Compared with BayesSpace，a recently developed method, SC-MEB is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. We then performed comprehensive simulation studies to demonstrate the effectiveness of SC-MEB over some existing methods. We first applied SC-MEB to perform clustering analysis in the spatial transcriptomics dataset from human dorsolateral prefrontal cortex tissues that were manually annotated. Our analysis results show that SC-MEB can achieve similar clustering performance with BayesSpace that uses the true number of clusters and fixed smoothness parameter. We then applied SC-MEB to analyze the dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap for identified signature genes shows that the identified clusters from SC-MEB is more separable than those from BayesSpace. By using pathway analysis, we identified three immune-related clusters and further compared mean expressions of COVID-19 signature genes in immune regions with those in non-immune ones.
报告人简介：华东师范大学统计交叉科学研究院副教授。主要研究兴趣为组学遗传学统计建模、高维大数据统计计算、生存分析。目前以第一作者在Nucleic Acids Research, Bioinformatics、SMMR、CSDA以及Genetic Epidemiology等期刊发表学术论文多篇。国际统计学会推选会员，完成国家自然科学基金青年项目一项。