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

BackgroundFeature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 102-105 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. MethodKTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets. ResultsThe 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. ConclusionsThe sample R code is available at https://github.com/tagtag/MultiR/