Joint identification of spatially variable genes via a network-assisted Bayesian regularization approach
发布时间:2026-01-26浏览量:论文摘要
Identifying genes that display spatial patterns is critical to investigating expression interactions within a spatial context and further dissecting biological understanding of complex mechanistic functionality. Despite the increase in statistical methods designed to identify spatially variable genes, they are mostly based on marginal analysis and share the limitation that the dependence (network) structures among genes are not well accommodated, where a biological process usually involves changes in multiple genes that interact in a complex network. In addition, the latent cellular composition within the spots can introduce confounding variations, negatively affecting the accuracy of the identification. In this study we develop a novel Bayesian regularization approach for spatial transcriptomic data, with confounding variations induced by varying cellular distributions effectively corrected. Significantly advancing from existing studies, a thresholded graph Laplacian regularization is proposed to simultaneously identify spatially variable genes and accommodate the network structure among genes. The proposed method is based on a zero-inflated negative binomial distribution, effectively accommodating the count nature, zero inflation, and overdispersion of spatial transcriptomic data. Extensive simulations and applications to real data demonstrate the competitive performance of the proposed method.
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作者介绍

吴明聪,中国人民大学统计学院2025级博士毕业生,主要研究方向为高维数据分析、变量选择、网络数据分析等。

李扬,中国人民大学吴玉章特聘教授、博士生导师,学校交叉科学学术委员会副主任、学校学位评定委员会委员,入选国家级青年人才项目;担任国际统计学会Elected Member、中国商业统计学会副会长、中国统计学会常务理事、中国现场统计研究会常务理事、北京生物医学统计与数据管理研究会监事长等;主要从事模型选择与不确定性评价、复杂调查设计与分析、潜变量建模、试验设计与推断等领域研究,在国内外知名期刊发表论文八十余篇,承担国家自然科学基金、教育部重大项目、全国统计科学研究重大项目等。

马双鸽,耶鲁大学生物统计系教授,国际统计学会推选会员、美国统计学会会士。研究主要集中于生物统计、遗传流行病学、生存分析、高维数据分析等。担任JASA, AISM, Briefings in Bioinformatics等多个国际期刊副主编。已在Nature Genetics、JASA、The Annals of Statistics、Biometrika、Briefings in Bioinformatics等国际权威期刊发表论文数百篇。

吴梦云,上海财经大学统计与数据科学学院教授。2013年获得中山大学概率论与数理统计博士学位,并于2016年8月至2018年7月在耶鲁大学生物统计系进行博士后研究。主要研究方向为高维数据变量选择、网络模型及整合分析等。目前,已在The Annals of Applied Statistics、Biometrics、Biostatistics、Statistics in Medicine、Bioinformatics等期刊发表多篇学术论文。入选上海市晨光计划、浦江人才以及启明星计划,主持国家自然科学青年基金和面上项目,以及全国统计科学研究重大项目。
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