Csala Attila, Hof Michel H, Zwinderman Aeilko H
Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, The Netherlands.
Biom J. 2019 Mar;61(2):406-423. doi: 10.1002/bimj.201700248. Epub 2018 Dec 3.
Redundancy Analysis (RDA) is a well-known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high-dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. We show that the multiset sparse Redundancy Analysis (multi-sRDA) framework is a prominent candidate for high-dimensional omics data analysis since it accounts for the directional information transfer between omics sets, and, through its sparse solutions, the interpretability of the result is improved. In this paper, we also describe a software implementation for multi-sRDA, based on the Partial Least Squares Path Modeling algorithm. We test our method through simulation and real omics data analysis with data sets of 364,134 methylation markers, 18,424 gene expression markers, and 47 cytokine markers measured on 37 patients with Marfan syndrome.
冗余分析（RDA）是一种用于描述相关数据集之间方向关系的著名方法。最近，我们提出了用于高维基因组数据分析的稀疏冗余分析（sRDA），以找到能够解释响应变量最大方差的解释变量。随着越来越多来自不同生物水平的生物分子数据可用，例如来自不同组学领域的基因型和表型数据，一个自然的研究方向是应用综合分析方法，以探索给定生物体某些表型的潜在生物学机制。我们表明，多集稀疏冗余分析（multi-sRDA）框架是高维组学数据分析的一个突出候选方法，因为它考虑了组学集之间的方向信息传递，并且通过其稀疏解，提高了结果的可解释性。在本文中，我们还描述了基于偏最小二乘路径建模算法的multi-sRDA的软件实现。我们通过对37名马凡综合征患者测量的364,134个甲基化标记、18,424个基因表达标记和47个细胞因子标记的数据集进行模拟和实际组学数据分析来测试我们的方法。
