Meta-analytic support vector machine for integrating multiple omics data. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Kim, SungHwan; Jhong, Jae-Hwan; Lee, JungJun; Koo, Ja-Yong
• Is Part Of: Biodata mining. Volume 10:Issue 1(2017)
• Page Start: 1
• Page End: 14
• Publication Date: 2017-12
• Subjects: Support vector machine -- Meta-analysis -- Data integration -- TCGA; Bioinformatics -- Periodicals; Computational biology -- Periodicals; Data mining -- Periodicals; 570.285
• DOI: 10.1186/s13040-017-0126-8
• Languages: English
• ISSNs: 1756-0381

Abstract

Abstract Background Of late, high-throughput microarray and sequencing data have been extensively used to monitor biomarkers and biological processes related to many diseases. Under this circumstance, the support vector machine (SVM) has been popularly used and been successful for gene selection in many applications. Despite surpassing benefits of the SVMs, single data analysis using small- and mid-size of data inevitably runs into the problem of low reproducibility and statistical power. To address this problem, we propose a meta-analytic support vector machine (Meta-SVM) that can accommodate multiple omics data, making it possible to detect consensus genes associated with diseases across studies. Results Experimental studies show that the Meta-SVM is superior to the existing meta-analysis method in detecting true signal genes. In real data applications, diverse omics data of breast cancer (TCGA) and mRNA expression data of lung disease (idiopathic pulmonary fibrosis; IPF) were applied. As a result, we identified gene sets consistently associated with the diseases across studies. In particular, the ascertained gene set of TCGA omics data was found to be significantly enriched in the ABC transporters pathways well known as critical for the breast cancer mechanism. Conclusion The Meta-SVM effectively achieves the purpose of meta-analysis as jointly leveraging multiple omics data, and facilitates identifying potential biomarkers and elucidating the disease process.