报告时间：2021年12月15日

               上午10:00-11:00

报告地点：腾讯会议

              （会议ID：337559510）

报告嘉宾：李向杰

报告主题：利用图卷积网络整合分析空间转录组数据、空间位置和组织学图像数据

报告摘要

SpaGCN: Integrating gene expression, Spatial location and histology to identify spatial domains and spatially variable genes by Graph Convolutional Network

Recent advances in spatial transcriptomics technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression analysis then detects genes with enriched expression patterns in the identified domains. Analyzing five spatially resolved transcriptomics datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than existing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, making it a desirable tool for spatial transcriptomics studies.

个人简介

李向杰，南开大学统计与数据科学学院讲师，2019-2021 在中国医学科学院阜外医院从事博士后研究工作，2019博士毕业于中国人民大学统计学院，2017-2018获国家留学基金委公派资助在美国宾夕法尼亚大学交流学习，主要研究方向为统计基因组学、生物信息学，临床应用等相关研究。主持博士后面上基金一项，参与国自然重大项目一项。在Nature Methods, Nature Communications, Nature Machine Intelligence, Genome Research, cardiovascular research, Briefings in Bioinformatics, Journal of statistical planning of inference, Journal of Statistical Computation and Simulation,《统计研究》,《统计与信息论坛》等杂志发表20多篇论文。