Meta-analytic support vector machine for integrating multiple omics data.
Meta-analytic support vector machine for integrating multiple omics data.
复制标题
荟萃分析支持向量机，用于集成多个OMIC数据。
DOI：
10.1186/s13040-017-0126-8
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发表时间：
2017
期刊：
影响因子：
4.5
通讯作者：
Koo JY
中科院分区：
文献类型：
--
作者：
Kim S;Jhong JH;Lee J;Koo JY
近年来，高通量微阵列和测序数据已广泛用于监测与许多疾病相关的生物标志物和生物过程。在这种情况下，支持向量机在基因选择中得到了广泛的应用并取得了成功。尽管支持向量机具有超越支持向量机的优势，但使用中小型数据的单一数据分析不可避免地会遇到再现性低和统计能力低的问题。为了解决这个问题，我们提出了一种元分析支持向量机（Meta-SVM），它可以容纳多个组学数据，从而可以在研究中发现与疾病相关的共识基因。实验研究表明，Meta-SVM在检测真实信号基因方面优于现有的meta分析方法。在实际数据应用中，应用了乳腺癌（TCGA）的多种组学数据和肺部疾病（特发性肺纤维化，IPF）的mRNA表达数据。因此，我们在研究中确定了与疾病一致的基因集。特别是，TCGA组学数据确定的基因集被发现在ABC转运蛋白通路中显著富集，这是众所周知的乳腺癌机制的关键。Meta-SVM有效地实现了联合利用多个组学数据进行meta分析的目的，有利于识别潜在的生物标志物和阐明疾病过程。本文的在线版本（doi:10.1186/s13040-017-0126-8）包含补充材料，可供授权用户使用。
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. 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. 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. The online version of this article (doi:10.1186/s13040-017-0126-8) contains supplementary material, which is available to authorized users.