Multiple confounders correction with regularized linear mixed effect models, with application in biological processes

Haohan Wang; Jingkang Yang

doi:10.1109/BIBM.2016.7822753

Multiple confounders correction with regularized linear mixed effect models, with application in biological processes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.

Original language	English (US)
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1561-1568
Number of pages	8
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822753
State	Published - 2016
Externally published	Yes
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: Dec 15 2016 → Dec 18 2016

Other

Other	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	12/15/16 → 12/18/16

Keywords

Confounding factors correction
Linear mixed model
Multiple confounding factors

ASJC Scopus subject areas

Genetics
Medicine (miscellaneous)
Genetics(clinical)
Biochemistry, medical
Biochemistry
Molecular Medicine
Health Informatics

Online availability

10.1109/BIBM.2016.7822753

Library availability

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Wang, H., & Yang, J. (2016). Multiple confounders correction with regularized linear mixed effect models, with application in biological processes. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1561-1568). Article 7822753 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822753

Multiple confounders correction with regularized linear mixed effect models, with application in biological processes. / Wang, Haohan; Yang, Jingkang.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1561-1568 7822753.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, H & Yang, J 2016, Multiple confounders correction with regularized linear mixed effect models, with application in biological processes. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822753, Institute of Electrical and Electronics Engineers Inc., pp. 1561-1568, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 12/15/16. https://doi.org/10.1109/BIBM.2016.7822753

Wang H, Yang J. Multiple confounders correction with regularized linear mixed effect models, with application in biological processes. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1561-1568. 7822753 doi: 10.1109/BIBM.2016.7822753

Wang, Haohan ; Yang, Jingkang. / Multiple confounders correction with regularized linear mixed effect models, with application in biological processes. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1561-1568

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title = "Multiple confounders correction with regularized linear mixed effect models, with application in biological processes",

abstract = "In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.",

keywords = "Confounding factors correction, Linear mixed model, Multiple confounding factors",

author = "Haohan Wang and Jingkang Yang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2016",

doi = "10.1109/BIBM.2016.7822753",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Yang, Jingkang

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N2 - In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.

AB - In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.

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KW - Linear mixed model

KW - Multiple confounding factors

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ER -

Multiple confounders correction with regularized linear mixed effect models, with application in biological processes

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Keywords

ASJC Scopus subject areas

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