编者荐语：

由AG代理基因赞助的CGM沙龙，此次为您带来哈佛大学、波士顿儿童医院/博德研究所Vijay Sankaran实验室博士后于福龙的分享。

CGM将于北京时间 04 月 28 日 星期四 10AM举办在线沙龙活动。本期活动，我们邀请到于福龙博士来讲述"通过网络传播算法进行单细胞分辨率的基因组变异功能研究  "，敬请期待！

于福龙，男，现哈佛大学、波士顿儿童医院/博德研究所Vijay Sankaran实验室博士后。2015-2018年取得哈尔滨医科大学博士学位，导师程书钧院士/李霞教授。2020年一月开始先后在Dana Farber 癌症研究所、波士顿儿童医院和博德研究所从事博士后研究工作。研究主要集中于单细胞生物学和癌症基因组学的相关生物信息学算法/软件及数据资源平台开发，以（共同）第一作者/（共同）通讯作者开发包括癌症相关的多组学数据分析平台和算法（Yu et al. Nucleic Acids Research, 2020; Yu et al. Briefings in Bioinformatics, 2019; Voit et al. Biorxiv, 2021）和单细胞数据分析算法和软件（Yu et al. Biorxiv, 2022; Yu et al. Bioinformatics, 2021; Zhang et al. Bioinformatics, 2020）等10篇文章。

With burgeoning human disease genetic associations and single-cell genomic atlases covering a range of tissues, there are unprecedented opportunities to systematically gain insights into the mechanisms of disease-causal variation. However, sparsity and noise, particularly in the context of single-cell epigenomic data, hamper the identification of disease- or trait-relevant cell types, states, and trajectories. To overcome these challenges, we have developed the SCAVENGE method, which maps causal variants to their relevant cellular context at single-cell resolution by employing the strategy of network propagation. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation including enrichment of blood traits at distinct stages of human hematopoiesis, defining monocyte subsets that increase the risk for severe coronavirus disease 2019 (COVID-19), and identifying intermediate lymphocyte developmental states that are critical for predisposition to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution, but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.