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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Vydané v:BMC medical genomics Ročník 15; číslo 1; s. 37 - 12
Hlavní autori: Taguchi, Y-h., Turki, Turki
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 24.02.2022
BMC
Predmet:
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
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Shrnutí: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/ .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1755-8794
1755-8794
DOI:10.1186/s12920-022-01181-4