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Integrative clustering methods for multi‐omics data

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Abstract Integrative analysis of multi‐omics data has drawn much attention from the scientific community due to the technological advancements which have generated various omics data. Leveraging these multi‐omics data potentially provides a more comprehensive view of the disease mechanism or biological processes. Integrative multi‐omics clustering is an unsupervised integrative method specifically used to find coherent groups of samples or features by utilizing information across multi‐omics data. It aims to better stratify diseases and to suggest biological mechanisms and potential targeted therapies for the diseases. However, applying integrative multi‐omics clustering is both statistically and computationally challenging due to various reasons such as high dimensionality and heterogeneity. In this review, we summarized integrative multi‐omics clustering methods into three general categories: concatenated clustering, clustering of clusters, and interactive clustering based on when and how the multi‐omics data are processed for clustering. We further classified the methods into different approaches under each category based on the main statistical strategy used during clustering. In addition, we have provided recommended practices tailored to four real‐life scenarios to help researchers to strategize their selection in integrative multi‐omics clustering methods for their future studies. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Applications of Computational Statistics > Genomics/Proteomics/Genetics Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
Three categories of integrative multi‐omics clustering methods. Multi‐omics data (e.g., DNA methylation, copy number variation, gene expression) are collected for each sample. Integrative multi‐omics clustering methods can be used to analyze such data and produce sample clusters. We summarized those methods into three categories. Concatenated clustering: combine the multi‐omics data into one matrix or search for the shared structure, followed by the final clustering; clustering of clusters: Obtain the clustering information from each omics dataset first and follow by the final clustering; interactive clustering: simultaneously integrate multi‐omics data and perform clustering
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Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
Applications of Computational Statistics > Genomics/Proteomics/Genetics
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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