Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis

Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 10 2 – 10 5 features. In particular, appropriate methods to weight individual omics datasets are unclear, and the approach adopted has substantial consequences...

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Bibliographic Details
Published in:BMC medical genomics Vol. 15; no. 1; pp. 37 - 12
Main Authors: Taguchi, Y-h., Turki, Turki
Format: Journal Article
Language:English
Published: London BioMed Central 24.02.2022
BMC
Subjects:
Biomedical and Life Sciences
Biomedicine
Data Analysis
Feature selection
Gene Expression
Genomics
Human Genetics
Kernel trick
Microarrays
Multiomcis
Proteomics
Tensor decomposition
Tensor decomposition
Feature selection
Kernel trick
Multiomcis
ISSN:1755-8794, 1755-8794
Online Access:Get full text
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Summary:Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 10 2 – 10 5 features. In particular, appropriate methods to weight individual omics datasets are unclear, and the approach adopted has substantial consequences for feature selection. In this study, we extended a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) method to integrate multi-omics datasets obtained from common samples in a weight-free manner. Method KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets. Results The proposed advanced KTD-based unsupervised FE method showed comparative performance to that of the previously proposed KTD method, as well as tensor decomposition-based unsupervised FE, but required reduced memory and central processing unit time. Moreover, this advanced KTD method, specifically designed for multi-omics analysis, attributes P values to features, which is rare for existing multi-omics–oriented methods. Conclusions The sample R code is available at https://github.com/tagtag/MultiR/ .
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ISSN:1755-8794
1755-8794
DOI:10.1186/s12920-022-01181-4