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

Yuichi Okinaga, Daisuke Kyogoku, Satoshi Kondo, Atsushi J. Nagano, Kei Hirose

研究成果: Contribution to journalArticle査読

抄録

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 , is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.

本文言語英語
論文番号11483
ジャーナルScientific reports
11
1
DOI
出版ステータス出版済み - 12 2021

All Science Journal Classification (ASJC) codes

  • 一般

フィンガープリント

「Relationship between gene regulation network structure and prediction accuracy in high dimensional regression」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル