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 approximatelyl 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/.