Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data

Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents...

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Bibliographic Details
Published in:BMC bioinformatics 2020-04, Vol.21 (1), p.141-141, Article 141
Main Authors: Min, Eun Jeong, Long, Qi
Format: Article
Language:English
Subjects:
Biomarkers
Biotechnology industries
Cancer
Computer simulation
Data analysis
Datasets
Eigenvalues
Gene expression
Gene network information
Genomics
Inertia
Integrative analysis
l 0 penalty
Methodology
Methods
Multiple co-inertia analysis
Multivariate analysis
Network penalty
Proteomics
Sparsity
Structural information
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Summary:Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease. Proposed sparse mCIA achieves simultaneous model estimation and feature selection and yields analysis results that are more interpretable than the existing mCIA. Furthermore, proposed structured sparse mCIA can effectively incorporate prior network information among genes, resulting in improved feature selection and enhanced interpretability.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-3455-4