Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring

Abstract Investigation of the genetic basis of traits or clinical outcomes heavily relies on identifying relevant variables in molecular data. However, characteristics such as high dimensionality and complex correlation structures of these data hinder the development of related methods, resulting in...

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Autores principales:	Yuan Zhou, Botao Fa, Ting Wei, Jianle Sun, Zhangsheng Yu, Yue Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/acbdf7fd5e124f8dbc69d0f1bcef190a
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spelling	oai:doaj.org-article:acbdf7fd5e124f8dbc69d0f1bcef190a2021-12-05T12:11:23ZElastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring10.1038/s41598-021-02706-02045-2322https://doaj.org/article/acbdf7fd5e124f8dbc69d0f1bcef190a2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02706-0https://doaj.org/toc/2045-2322Abstract Investigation of the genetic basis of traits or clinical outcomes heavily relies on identifying relevant variables in molecular data. However, characteristics such as high dimensionality and complex correlation structures of these data hinder the development of related methods, resulting in the inclusion of false positives and negatives. We developed a variable importance measure method, termed the ECAR scores, that evaluates the importance of variables in the dataset. Based on this score, ranking and selection of variables can be achieved simultaneously. Unlike most current approaches, the ECAR scores aim to rank the influential variables as high as possible while maintaining the grouping property, instead of selecting the ones that are merely predictive. The ECAR scores’ performance is tested and compared to other methods on simulated, semi-synthetic, and real datasets. Results showed that the ECAR scores improve the CAR scores in terms of accuracy of variable selection and high-rank variables’ predictive power. It also outperforms other classic methods such as lasso and stability selection when there is a high degree of correlation among influential variables. As an application, we used the ECAR scores to analyze genes associated with forced expiratory volume in the first second in patients with lung cancer and reported six associated genes.Yuan ZhouBotao FaTing WeiJianle SunZhangsheng YuYue ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuan Zhou Botao Fa Ting Wei Jianle Sun Zhangsheng Yu Yue Zhang Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring
description	Abstract Investigation of the genetic basis of traits or clinical outcomes heavily relies on identifying relevant variables in molecular data. However, characteristics such as high dimensionality and complex correlation structures of these data hinder the development of related methods, resulting in the inclusion of false positives and negatives. We developed a variable importance measure method, termed the ECAR scores, that evaluates the importance of variables in the dataset. Based on this score, ranking and selection of variables can be achieved simultaneously. Unlike most current approaches, the ECAR scores aim to rank the influential variables as high as possible while maintaining the grouping property, instead of selecting the ones that are merely predictive. The ECAR scores’ performance is tested and compared to other methods on simulated, semi-synthetic, and real datasets. Results showed that the ECAR scores improve the CAR scores in terms of accuracy of variable selection and high-rank variables’ predictive power. It also outperforms other classic methods such as lasso and stability selection when there is a high degree of correlation among influential variables. As an application, we used the ECAR scores to analyze genes associated with forced expiratory volume in the first second in patients with lung cancer and reported six associated genes.
format	article
author	Yuan Zhou Botao Fa Ting Wei Jianle Sun Zhangsheng Yu Yue Zhang
author_facet	Yuan Zhou Botao Fa Ting Wei Jianle Sun Zhangsheng Yu Yue Zhang
author_sort	Yuan Zhou
title	Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring
title_short	Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring
title_full	Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring
title_fullStr	Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring
title_full_unstemmed	Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring
title_sort	elastic correlation adjusted regression (ecar) scores for high dimensional variable importance measuring
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/acbdf7fd5e124f8dbc69d0f1bcef190a
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_version_	1718372212570849280

Elastic Correlation Adjusted Regression (ECAR) scores for high dimensional variable importance measuring

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