Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction

Y-H Taguchi; Turki Turki

doi:10.3389/frai.2023.1237542

Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction

Front Artif Intell. 2023 Sep 1:6:1237542. doi: 10.3389/frai.2023.1237542. eCollection 2023.

Authors

Y-H Taguchi¹, Turki Turki²

Affiliations

¹ Department of Physics, Chuo University, Tokyo, Japan.
² Department of Computer Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract

Motivation: Tensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.

Results: We developed two bioconductor packages-TDbasedUFE and TDbasedUFEadv-that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO.

Availability and implementation: TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.org/packages/TDbasedUFEadv, respectively.

Keywords: feature selection; gene expression; multiomics; tensor decomposition; unsupervised learning.

Grants and funding

This study was supported in part by funds from the Chuo University (TOKUTEI KADAI KENKYU).