Affiliation:
1. School of Statistics and Management, Shanghai University of Finance and Economics
2. Department of Biostatistics at Yale University
Abstract
Abstract
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.
Funder
National Institutes of Health
National Science Foundation
Pilot Award from Yale Cancer Center
Bureau of Statistics of China
Shanghai Education Development Foundation
Shanghai Municipal Education Commission
Shanghai University of Finance and Economics
Shanghai Pujiang Program
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
7 articles.
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