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

Y-H Taguchi¹, Turki Turki²

¹Department of Physics, Chuo University, Tokyo, Japan.

Frontiers in Artificial Intelligence

|September 18, 2023

Summary

This summary is machine-generated.

New R packages, TDbasedUFE and TDbasedUFEadv, make tensor decomposition (TD)-based unsupervised feature extraction (FE) accessible to non-experts. These tools aid in identifying differentially expressed genes and performing multiomics analysis, outperforming existing methods.

Keywords:

feature selection gene expression multiomics tensor decomposition unsupervised learning

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Tensor decomposition (TD)-based unsupervised feature extraction (FE) is effective for bioinformatics tasks like biomarker identification and gene analysis.
Widespread adoption of TD-based FE has been limited by a lack of user-friendly tools for researchers without specialized expertise.

Purpose of the Study:

To develop accessible tools for TD-based unsupervised FE in bioinformatics.
To enable researchers unfamiliar with TD to perform advanced analyses such as differential gene expression and multiomics analysis.

Main Methods:

Development of two R/Bioconductor packages: TDbasedUFE and TDbasedUFEadv.
Implementation of TD-based unsupervised FE algorithms within user-friendly interfaces.
Facilitation of differential gene expression analysis and multiomics data integration.

Main Results:

The developed packages, TDbasedUFE and TDbasedUFEadv, successfully enable TD-based unsupervised FE for non-experts.
TDbasedUFE demonstrated superior performance compared to state-of-the-art methods like DESeq2 and DIABLO in relevant analyses.
The packages facilitate key bioinformatics tasks including identification of differentially expressed genes and multiomics analysis.

Conclusions:

TDbasedUFE and TDbasedUFEadv lower the barrier to entry for utilizing powerful TD-based FE methods in bioinformatics research.
These packages offer a robust and user-friendly alternative for feature extraction and analysis in genomics and multiomics studies.
The tools are freely available, promoting broader application and advancement in the field.