Abstract
Identification of meaningful connections from different types of omics data sets is extremely important in computational biology and system biology. Integration of multi-omics data is the first essential step for data analysis, but it is also challenging for the systems biologists to correctly integrate different data together. Practical comparison of different omics data integration methods can provide biomedical researchers a clear view of how to select appropriate methods and tools to integrate and analyze different multi-omics datasets. Here we illustrate two widely used R-based omic data integration tools: mixOmics and STATegRa, to analyze different types of omics data sets and evaluate their performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Castelo, R., Roverato, A.: Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comput. Biol. 16, 213 (2009)
de Diego, R.H., et al.: STATegra EMS: an Experiment Management System for complex next-generation omics experiments. BMC Syst. Biol. 8, S9 (2014)
Fukushima, A.: DiffCorr: an R package to analyze and visualize differential correlations in biological network. Gene 518, 209 (2013)
Hawkins, R., Hon, G., Ren, B.: Next-generation genomics: an integrative approach. Nat. Rev. Genet. 11, 476–86 (2010)
Huang, S., Chaudhary, K., Garmire, L.: More is better: recent progress in multi-omics data integration methods. Front. Genet. 8, 84 (2017)
Martin, P.: Novel aspects of PPAR\(\alpha \)-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study. Hepatology 45, 767–777 (2007)
Rajasundaram, D., Selbig, J.: More effort-more results: recent advances in integrative ‘omics data analysis. Curr. Opin. Plant Biol. 30, 57–61 (2016)
Rohart, F., et al.: mixOmics: an R package for omics feature selection and multiple data integration. PLOS Comput. Biol. 13, e1005752 (2017)
Zhao, T., et al.: The huge package for high-dimensional undirected graph estimation in R. J. Mach. Learn. Res. 13, 1059 (2012)
Acknowledgements
H.G. is partially supported by the NIH grant 1R15GM129696-01A1 and Australian National University; T.-H.A is supported by NSF CRII-156629, NSF-1564894, and Saint Louis University President’s Research Fund (PRF).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Feng, W., Yu, Z., Kang, M., Gong, H., Ahn, TH. (2020). Practical Evaluation of Different Omics Data Integration Methods. In: Shaban-Nejad, A., Michalowski, M. (eds) Precision Health and Medicine. W3PHAI 2019. Studies in Computational Intelligence, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-030-24409-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-24409-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24408-8
Online ISBN: 978-3-030-24409-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)