2022年2月24日
Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis
BMC Medical Genomics
- ,
- 巻
- 15
- 号
- 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/.
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