Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

Abstract The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance s...

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Autores principales:	Yuichi Okinaga, Daisuke Kyogoku, Satoshi Kondo, Atsushi J. Nagano, Kei Hirose
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/38c9865acb7641c6914c6399fe62353d
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spelling	oai:doaj.org-article:38c9865acb7641c6914c6399fe62353d2021-12-02T17:51:29ZRelationship between gene regulation network structure and prediction accuracy in high dimensional regression10.1038/s41598-021-90791-62045-2322https://doaj.org/article/38c9865acb7641c6914c6399fe62353d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90791-6https://doaj.org/toc/2045-2322Abstract The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. When the gene regulation network is a random graph, a sufficiently large number of observations are required to ensure good prediction accuracy with the lasso. The PCR provided poor prediction accuracy regardless of the sample size. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicates that a relatively small number of observations, such as $$N=300$$ N = 300 , is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.Yuichi OkinagaDaisuke KyogokuSatoshi KondoAtsushi J. NaganoKei HiroseNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuichi Okinaga Daisuke Kyogoku Satoshi Kondo Atsushi J. Nagano Kei Hirose Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
description	Abstract The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. When the gene regulation network is a random graph, a sufficiently large number of observations are required to ensure good prediction accuracy with the lasso. The PCR provided poor prediction accuracy regardless of the sample size. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicates that a relatively small number of observations, such as $$N=300$$ N = 300 , is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.
format	article
author	Yuichi Okinaga Daisuke Kyogoku Satoshi Kondo Atsushi J. Nagano Kei Hirose
author_facet	Yuichi Okinaga Daisuke Kyogoku Satoshi Kondo Atsushi J. Nagano Kei Hirose
author_sort	Yuichi Okinaga
title	Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
title_short	Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
title_full	Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
title_fullStr	Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
title_full_unstemmed	Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
title_sort	relationship between gene regulation network structure and prediction accuracy in high dimensional regression
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/38c9865acb7641c6914c6399fe62353d
work_keys_str_mv	AT yuichiokinaga relationshipbetweengeneregulationnetworkstructureandpredictionaccuracyinhighdimensionalregression AT daisukekyogoku relationshipbetweengeneregulationnetworkstructureandpredictionaccuracyinhighdimensionalregression AT satoshikondo relationshipbetweengeneregulationnetworkstructureandpredictionaccuracyinhighdimensionalregression AT atsushijnagano relationshipbetweengeneregulationnetworkstructureandpredictionaccuracyinhighdimensionalregression AT keihirose relationshipbetweengeneregulationnetworkstructureandpredictionaccuracyinhighdimensionalregression
_version_	1718379219159875584

Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

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