Table 1.

Reviews on Integrating Multi-level Omics Data (a partial list).

Reference	Type	Description
Richardson et al. [10]	Comprehensive	Review statistical methods for both vertical integration and horizontal integration. Introduce different types of genomic data (DNA, Epigenetic marks, RNA and protein), genomics data resources and annotation databases.
Bersanelli et al. [11]	Comprehensive	Review mathematical and methodological aspects of data integration methods, with the following four categories (1) network-free non-Bayesian, (2) network-free Bayesian, (3) network-based non-Bayesian and (4) network-based Bayesian.
Hasin et al. [12]	Comprehensive	Different from the studies with emphasis on statistical integration methods, this review focuses on biological perspectives, i.e., the genome first approach, the phenotype first approach and the environment first approach.
Huang et al. [13]	Comprehensive	This review summarizes published integration studies, especially the matrix factorization methods, Bayesian methods, network based methods and multiple kernel learning methods.
Li et al. [14]	Comprehensive	Review the integration of multi-view biological data from the machine learning perspective. Reviewed methods include Bayesian models and networks, ensemble learning, multi-modal deep learning and multi-modal matrix/tensor factorization.
Pucher et al. [15]	Comprehensive (with case study)	Review three methods, sCCA, NMF and MALA and assess the performance on pairwise integration of omics data. Examine the consistence among results identified by different methods.
Yu et al. [16]	Comprehensive	This study first summarizes data resources (genomics, transcriptome, epigenomics, metagenomics and interactome) and data structure (vector, matrix, tensor and high-order cube). Methods are reviewed mainly following the bottom-up integration and top-down integration.
Zeng et al. [17]	Comprehensive	The statistical learning methods are overviewed from the following aspects: exploratory analysis, clustering methods, network learning, regression based learning and biological knowledge enrichment learning.
Rappoport et al. [18]	Clustering (with case study)	Review studies conducting joint clustering of multi-level omics data. Comprehensively assess the performance of nine clustering methods on ten types of cancer from TCGA.
Tini et al. [19]	Unsupervised integration (with case study)	Evaluation of five unsupervised integration methods on BXD, Platelet, BRCA data sets, as well as simulated data. Investigate the influences of parameter tuning, complexity of integration (noise level) and feature selection on the performance of integrative analysis.
Chalise et al. [20]	Clustering (with case study)	Investigate the performance of seven clustering methods on single-level data and three clustering methods on multi-level data.
Wang et al. [21]	Clustering	Discuss the clustering methods in three major groups: direct integrative clustering, clustering of clusters and regulatory integrative clustering. This study is among the first to review integrative clustering with prior biological information such as regulatory structure, pathway and network information.
Ickstadt et al. [22]	Bayesian	Review integrative Bayesian methods for gene prioritization, subgroup identification via Bayesian clustering analysis, omics feature selection and network learning.
Meng et al. [23]	Dimension Reduction (with case study)	Review dimension reduction methods for integration and examine visualization and interpretation of simultaneous exploratory analyses of multiple data sets based on dimension reduction.
Rendleman et al. [24]	Proteogenomics	This study is not another review on the statistical integrative methods. Instead, it discusses integration with an emphasis on the mass spectrometry-based proteomics data.
Yan et al. [25]	Graph- and kernel-based (with case study)	Graph- and kernel- based integrative methods have been systematically reviewed and compared using GAW 19 data and TCGA Ovarian and Breast cancer data in this study. Kernel-based methods are generally more computationally expensive. They lead to more complicated but better models than those obtained from the graph-based integrative methods.
Wu et al. [present review]	Variable Selection based	This review investigates existing multi-omics integrating studies from the variable selection point of view. This new perspective sheds fresh insight on integrative analysis.