TY - JOUR
T1 - Multiset sparse redundancy analysis for high-dimensional omics data
AU - Csala, Attila
AU - Hof, Michel H.
AU - Zwinderman, Aeilko H.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85058052961&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/30506971
U2 - https://doi.org/10.1002/bimj.201700248
DO - https://doi.org/10.1002/bimj.201700248
M3 - Article
C2 - 30506971
SN - 0323-3847
VL - 61
SP - 406
EP - 423
JO - Biometrical journal. Biometrische Zeitschrift
JF - Biometrical journal. Biometrische Zeitschrift
IS - 2
ER -