Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis
Background Feature 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...
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| Vydané v: | BMC medical genomics Ročník 15; číslo 1; s. 37 - 12 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
24.02.2022
BMC |
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| ISSN: | 1755-8794, 1755-8794 |
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| Abstract | Background
Feature 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.
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/
. |
|---|---|
| AbstractList | Background
Feature 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.
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 [Formula: see text]-[Formula: see text] 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. KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets. 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. The sample R code is available at https://github.com/tagtag/MultiR/ . Abstract Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately $$10^2$$ 10 2 – $$10^5$$ 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. 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 [Formula: see text]-[Formula: see text] 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.BACKGROUNDFeature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately [Formula: see text]-[Formula: see text] 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.KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets.METHODKTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets.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.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.The sample R code is available at https://github.com/tagtag/MultiR/ .CONCLUSIONSThe sample R code is available at https://github.com/tagtag/MultiR/ . |
| ArticleNumber | 37 |
| Author | Taguchi, Y-h. Turki, Turki |
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| Keywords | Tensor decomposition Feature selection Kernel trick Multiomcis |
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Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately
10
2
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5... Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately [Formula: see text]-[Formula:... Abstract Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately $$10^2$$... |
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| SubjectTerms | Biomedical and Life Sciences Biomedicine Data Analysis Feature selection Gene Expression Genomics Human Genetics Kernel trick Microarrays Multiomcis Proteomics Tensor decomposition |
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| Title | Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis |
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