Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa169
Keywords
gene expression data; independent and overlapping information; regulators; vertical data integration
Funding
- National Institutes of Health [CA241699, CA216017]
- National Science Foundation [1916251]
- Yale Cancer Center
- Shanghai Education Development Foundation
- Shanghai Municipal Education Commission [18CG42]
- Program for Innovative Research Team of Shanghai University of Finance and Economics
- Shanghai Pujiang Program [19PJ1403600]
- Bureau of Statistics of China [2018LD02]
- Chenguang Program
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1916251] Funding Source: National Science Foundation
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This article presents selective review of vertical data integration methods for gene expression data, covering both marginal and joint analysis as well as supervised and unsupervised analysis, to provide a sketch of the vertical data integration paradigm and briefly discuss potential pitfalls and future directions.
Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a 'lack of information' problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.
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