Abstract
BackgroundFeature 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.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/.
Highlights
Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 102–105 features
The proposed advanced kernel tensor decomposition (KTD)-based unsupervised feature extraction (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. This advanced KTD method, designed for multi-omics analysis, attributes P values to features, which is rare for existing multi-omics–oriented methods
KTD-based unsupervised FE successfully selected features correlated with aj
Summary
Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 102–105 features. Among the numerous proposed methods adapted to multi-omics data analysis [1, 2], only few are capable of performing feature selection. In multi-omics analysis, it is difficult to obtain large sample sizes since multiple observations, each of which corresponds to individual omics approaches, must be performed. In this sense, the required cost and time. Taguchi and Turki BMC Medical Genomics (2022) 15:37 are multiplied in proportion to the number of omics approaches considered This often results in a smaller number of samples to which multi-omics measurements are performed when only limited experimental resources are available
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
