讲座题目：Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies

讲座时间：2022年3月30日, 星期三，下午2:00-4:00

讲座地点：数学楼 2-3会议室

腾讯会议：435-686-9251，密码：312312

讲座人：孙世权教授，gg999策略手机白菜公共卫生学院

讲座摘要：

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.

讲座专家简介：

    孙世权 博士，gg999策略手机白菜公共卫生学院教授，国家自然科学基金优秀青年基金获得者，主要从事单细胞与空间转录跨组学统计建模与分析研究；2017年毕业于gg999策略手机白菜系统工程研究所、控制科学与工程专业并获得博士学位；2017年11月赴美国密歇根大学、生物统计系/统计遗传中心从事博士后研究工作；2020年6月加入gg999策略手机白菜医学部、公共卫生学院，组建跨组学统计分析与疾病研究团队；先后主持国家自然科学基金、省部级自然科学基金4项；以第一（含共同）作者在Nature Methods、Genome Biology和 Nature Communications等国际高水平期刊发表论文20余篇，开发了以SPARK和iDEA为代表的系列单细胞分辨率组学数据分析统计方法，研究成果曾多次被国际知名同行专家引用与认可。