論文

査読有り 筆頭著者 責任著者 国際共著 国際誌
2022年2月24日

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

BMC Medical Genomics
  • Y-h. Taguchi
  • ,
  • Turki Turki

15
1
開始ページ
37
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1186/s12920-022-01181-4
出版者・発行元
Springer Science and Business Media LLC

Background
Feature 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.

Method
KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets.

Results
The 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.

Conclusions
The sample R code is available at https://github.com/tagtag/MultiR/.

リンク情報
DOI
https://doi.org/10.1186/s12920-022-01181-4
共同研究・競争的資金等の研究課題
PCA及びTDを用いた教師無し学習による変数選択法によるscRNA-seq解析
共同研究・競争的資金等の研究課題
テンソル分解を用いた教師無し学習による変数選択法を用いたトランスオミクス解析
共同研究・競争的資金等の研究課題
テンソル分解を用いた教師無し学習による変数選択法のヒストン修飾解析への応用
URL
https://link.springer.com/content/pdf/10.1186/s12920-022-01181-4.pdf
URL
https://link.springer.com/article/10.1186/s12920-022-01181-4/fulltext.html
ID情報
  • DOI : 10.1186/s12920-022-01181-4
  • eISSN : 1755-8794

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