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

Taguchi, Y.-h.; Turki, T.

doi:10.1101/2021.05.21.445049

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bioRxiv · 10.1101/2021.05.21.445049

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

Taguchi, Y.-h.·Turki, T.

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Abstract

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/

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Taguchi, Y.-h., Turki, T.. 2021-05-23. Novel feature selection via kernel tensor decomposition for improved multi-omics data analysis. https://doi.org/10.1101/2021.05.21.445049

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