Next Article in Journal
Inhibitor of Hyaluronic Acid Synthesis 4-Methylumbelliferone as an Anti-Inflammatory Modulator of LPS-Mediated Astrocyte Responses
Next Article in Special Issue
Transcriptional Profiling of Whisker Follicles and of the Striatum in Methamphetamine Self-Administered Rats
Previous Article in Journal
Chemical Space Exploration of Oxetanes
Previous Article in Special Issue
Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference
Article

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components

1
Department of Preventive Medicine, Eulji University, Daejeon 34824, Korea
2
Department of Statistics, Korea University, Seoul 02841, Korea
3
Department of Statistics, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(21), 8202; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218202
Received: 22 September 2020 / Revised: 27 October 2020 / Accepted: 31 October 2020 / Published: 2 November 2020
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables. View Full-Text
Keywords: dimensional reduction; multi-omics data; sparse principal component analysis; variable clustering dimensional reduction; multi-omics data; sparse principal component analysis; variable clustering
Show Figures

Figure 1

MDPI and ACS Style

Park, M.; Kim, D.; Moon, K.; Park, T. Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components. Int. J. Mol. Sci. 2020, 21, 8202. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218202

AMA Style

Park M, Kim D, Moon K, Park T. Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components. International Journal of Molecular Sciences. 2020; 21(21):8202. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218202

Chicago/Turabian Style

Park, Mira, Doyoen Kim, Kwanyoung Moon, and Taesung Park. 2020. "Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components" International Journal of Molecular Sciences 21, no. 21: 8202. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218202

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop