报告人：刘学艳
工作单位： University of New Orleans, USA
报告题目：A correlation method for colocalization analysis in dual-color super-resolution microscopy images
报告时间：2026年6月12日（周五）9:00-11:00
报告地点：D2-220
摘要：Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy (STORM), provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false positive errors. In this paper we develop a novel statistical method, along with an R package and on-line app, to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of LC3-LAMP1 protein co-localization in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science and ecology.
报告人简介：刘学艳，美国路易斯安那州University of New Orleans数学系教授。2001年吉林大学数学本科，2004年中国海洋大学数学硕士，2013年美国Baylor University数学博士学位。2013-2016年在美国University of Tennessee at Chattanooga做访问助理教授，2016-2018年在美国St. Jude Children’s Research Hospital生物统计系做博士后研究。目前研究方向主要是空间统计学，生物统计，应用统计等。已发表30余篇学术论文，2本研究生教材。
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