A0168
Title: Multi-way overlapping clustering by Bayesian tensor decomposition
Authors: Zhuofan Wang - Institute of Statistics and Big Data, Renmin University of China (China) [presenting]
Abstract: The development of modern sequencing technologies provides great opportunities to measure gene expression of multiple tissues from different individuals. The three-way variation across genes, tissues, and individuals makes statistical inference challenging. A Bayesian multi-way clustering approach is proposed to cluster genes, tissues, and individuals simultaneously. The proposed model adaptively trichotomizes the observed data into three latent categories and uses a Bayesian hierarchical construction to further decompose the latent variables into lower-dimensional features, which can be interpreted as overlapping clusters. With a Bayesian nonparametric prior, i.e., the Indian buffet process, the method determines the cluster number automatically. The utility of the approach is demonstrated through simulation studies and an application to the genotype-tissue expression (GTEx) RNA-seq data. The clustering result reveals some interesting findings about depression-related genes in the human brain, which are also consistent with biological domain knowledge.
