A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data

Abstract

Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features.

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

“…The Z matrix is used as in iCluster for the clustering. The latest extension of iCluster, which builds on iCluster+, is iClusterBayes [66]. This method replaces the regularization in iCluster+ with full Bayesian regularization.…”

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

“…Algorithms with similar aims that decompose aggregations of data matrices into lower dimension spaces have also been developed [Chalise and Fridley, 2017, Chalise et al, 2014, Chen et al, 2008. Mo et al [2017]. A Bayesian model makes the common boundary assumption and additionally encodes sparsity into the model via use of priors on variable inclusion.…”

PAMOGK: A Pathway Graph Kernel based Multi-Omics Clustering Approach for Discovering Cancer Patient Subgroups

“…We compare PAMOGK with eight other multi-omics methods. These include kmeans [26], MCCA [54], LRACluster [55], rMKL-LPP [45], iClusterBayes [29], PINS [34], SNF [51], and finally Spectral Clustering [58]. These methods cover all methods that are included in a recent comparative benchmark study by Rappoport et al [36] with the exception of multiNMF [24], which we are not able to run properly.…”

“…There are sophisticated early integration approaches that aim to overcome these problems. iClusterBayes and its earlier variants [42,30,29] and LRACluster assume a latent lower dimensional distribution of data and uses regularization. A different strategy is to deploy late integration approaches, in which the samples are clustered with each omic data type separately, and the ensemble's cluster assignments are combined into a single solution.…”