报  告  人：蔡嘉辰（Stanford University, Gladstone Institutes）

报告时间：12月13日（周五）15：00-17：00

报告地点：数学楼（海纳苑2幢 ）203室

报告摘要: The increasing availability of high-dimensional, longitudinal measures of gene expression can facilitate understanding of biological mechanisms, as required for precision medicine. Biological knowledge suggests that it may be best to describe complex diseases at the level of underlying pathways, which may interact with one another. We propose a Bayesian approach that allows for characterizing such correlation among different pathways through dependent Gaussian processes (DGP) and mapping the observed high-dimensional gene expression trajectories into unobserved low-dimensional pathway expression trajectories via Bayesian sparse factor analysis. Our proposal is the first attempt to relax the classical assumption of independent factors for longitudinal data and has demonstrated a superior performance in recovering the shape of pathway expression trajectories, revealing the relationships between genes and pathways, and predicting gene expressions (closer point estimates and narrower predictive intervals), as demonstrated through simulations and real data analysis. To fit the model, we propose a Monte Carlo expectation maximization (MCEM) scheme that can be implemented conveniently by combining a standard Markov Chain Monte Carlo sampler and an R package GPFDA,which returns the maximum likelihood estimates of DGP hyperparameters. The modular structure of MCEM makes it generalizable to other complex models involving the DGP model component. Our R package DGP4LCF that implements the proposed approach is available on the Comprehensive R Archive Network (CRAN).

报告人简介：蔡嘉辰，2024年获剑桥大学生物统计学博士学位，目前在斯坦福大学生物医学数据科学系计算生物学家Barbara Engelhardt教授指导下从事博士后研究工作。主要研究方法为贝叶斯统计，高斯过程；应用领域为多组学数据。近年来在国际知名期刊发表论文7篇，其中第一作者论文3篇，发表的杂志为Biometrics, Journal of the Royal Statistical Society Series C: Applied Statistics, Statistics in Medicine等。