数学与统计学院学术报告[2020] 081号

(高水平大学建设系列报告434号)

报告题目:  Bayesian joint modeling of single-cell expression data and bulk spatial transcriptomic data

报告人：罗翔宇（中国人民大学统计与大数据研究院）

报告时间：2020年10月30日上午11:05-11:50　　　　　　　　　　　　　 　

报告地点： 腾讯会议 会议号码：701968444　　 　　　　　　　　　　　　　　　　　

报告内容： Single-cell RNA-sequencing (scRNA-seq) enables gene expression profiling at single-cell resolution, but it loses the spatial information of cells for solid tissues during the tissue dissociation step before sequencing. In contrast, bulk spatial transcriptomics (ST) methods can measure the expression of spatially organized spots in solid tissues, but as a spot comprises dozens of cells, ST expression levels are averaged signals and lack cellular resolution. Joint analysis of these two complementary data types provides the opportunity to recover the spatial patterns of cell types and obtain the cellular enrichment of spots. However, there is a lack of unified statistical methods to achieve this goal. This study develops a Bayesian statistical method named BEATS to jointly model scRNA-seq data and bulk ST data from a common sample in the presence of cellular and spatial heterogeneity. BEATS can simultaneously (a) discover cell types, where cells in a cell type share mean expression profiles; (b) identify spot regions, where a region is a set of spots with the same cellular compositions; and (c) estimate cell-type proportions for each spot region. The Bayesian posterior inference is performed through a hybrid Markov chain Monte Carlo sampling algorithm. A simulation study and application to datasets on pancreatic ductal adenocarcinoma tissues demonstrate the practical utility of BEATS.

报告人简历: 罗翔宇，中国人民大学统计与大数据研究院助理教授、博士生导师。2018年博士毕业于香港中文大学统计系，2014年本科毕业于中国科学技术大学统计与金融系。研究兴趣为贝叶斯统计、生物信息学、统计计算等。目前主持一项国家自然科学基金青年项目。已有研究成果发表在Journal of the American Statistical Association, Annals of Applied Statistics, Nature Communications等统计或生物信息国际期刊上。

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数学与统计学院

2020年10月28日