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Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

View ORCID ProfileY-h. Taguchi, View ORCID ProfileTurki Turki
doi: https://doi.org/10.1101/2022.02.18.481115
Y-h. Taguchi
1Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, 112-8551 Tokyo, JAPAN
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Turki Turki
2Department of Computer Science, King Abdulaziz University, 21589 Jeddah, Saudi Arabia
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Abstract

Background Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes’ identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P-values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution.

Results Optimizing the standard deviation such that the histogram of P-values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes.

Conclusions Tensor decomposition- and principal component analysis-based unsupervised feature extraction are perhaps better than state-of-art methods in regard to predicting differentially expressed genes because they achieve the desired property that the less expressed differentially expressed genes should be less likely selected or even associated with the same amount of logarithmic fold change, although they assume neither negative binomial distribution nor dispersion relation, which is usually assumed in state-of-art methods.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 22, 2022.
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Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods
Y-h. Taguchi, Turki Turki
bioRxiv 2022.02.18.481115; doi: https://doi.org/10.1101/2022.02.18.481115
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Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods
Y-h. Taguchi, Turki Turki
bioRxiv 2022.02.18.481115; doi: https://doi.org/10.1101/2022.02.18.481115

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