中国人民大学在读博士闫引桥：空间转录组学研究中的贝叶斯整合区域分割方法

报告摘要
The spatially resolved transcriptomic study is a recently developed biological experiment that can measure gene expressions and retain spatial information simultaneously, opening a new avenue to characterize fine-grained tissue structures. We propose a nonparametric Bayesian method named BINRES to carry out the region segmentation for a tissue section by integrating all the three types of data generated during the study---gene expressions, spatial coordinates, and the histology image. BINRES is able to capture more subtle regions than existing statistical partitioning models that only partially make use of the three data modes and is more interpretable than neural-network-based region segmentation approaches. Specifically, due to a nonparametric spatial prior, BINRES does not require a prespecified region number and can learn it automatically. BINRES also combines the image and the gene expressions in the Bayesian consensus clustering framework and thus flexibly adjusts their contribution weights in a data-adaptive manner. A computationally scalable extension is developed for large-scale studies. Both simulation studies and the real application to mouse spatial transcriptomic datasets demonstrate that BINRES outperforms the competing methods and easily achieves the uncertainty quantification of the integrative partition. The R package of the proposed method is publicly available at
https://github.com/yinqiaoyan/BINRES

This is a joint work with Xiangyu Luo.

嘉宾简介
闫引桥，中国人民大学统计与大数据研究院2019级博士生，导师为罗翔宇副教授，研究兴趣为贝叶斯统计、非参数贝叶斯方法、生物信息学、统计计算等，目前已有研究发表在Journal of Computational and Graphical Statistics，Journal of the American Statistical Association期刊上，曾获得第二十四届京津冀青年概率统计学术研讨会颁发的“钟家庆优秀论文奖”。


直播分享时间：2024年5月11日