Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies

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.