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Meta-Analysis Based on Nonconvex Regularization

The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostat...

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Autores principales: Zhang, Hui, Li, Shou-Jiang, Zhang, Hai, Yang, Zi-Yi, Ren, Yan-Qiong, Xia, Liang-Yong, Liang, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113298/
https://www.ncbi.nlm.nih.gov/pubmed/32238826
http://dx.doi.org/10.1038/s41598-020-62473-2
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author Zhang, Hui
Li, Shou-Jiang
Zhang, Hai
Yang, Zi-Yi
Ren, Yan-Qiong
Xia, Liang-Yong
Liang, Yong
author_facet Zhang, Hui
Li, Shou-Jiang
Zhang, Hai
Yang, Zi-Yi
Ren, Yan-Qiong
Xia, Liang-Yong
Liang, Yong
author_sort Zhang, Hui
collection PubMed
description The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L(1/2) regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clipped Absolute Deviation regularization (meta-SCAD). The three nonconvex regularization methods are effective approaches for variable selection developed in recent years. Through the hierarchical decomposition of coefficients, our methods not only maintain the flexibility of variable selection and improve the efficiency of selecting important biomarkers, but also summarize and synthesize scientific evidence from multiple studies to consider the relationship between different datasets. We give the efficient algorithms and the theoretical property for our methods. Furthermore, we apply our methods to the simulation data and three publicly available lung cancer gene expression datasets, and compare the performance with state-of-the-art methods. Our methods have good performance in simulation studies, and the analysis results on the three publicly available lung cancer gene expression datasets are clinically meaningful. Our methods can also be extended to other areas where datasets are heterogeneous.
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spelling pubmed-71132982020-04-06 Meta-Analysis Based on Nonconvex Regularization Zhang, Hui Li, Shou-Jiang Zhang, Hai Yang, Zi-Yi Ren, Yan-Qiong Xia, Liang-Yong Liang, Yong Sci Rep Article The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L(1/2) regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clipped Absolute Deviation regularization (meta-SCAD). The three nonconvex regularization methods are effective approaches for variable selection developed in recent years. Through the hierarchical decomposition of coefficients, our methods not only maintain the flexibility of variable selection and improve the efficiency of selecting important biomarkers, but also summarize and synthesize scientific evidence from multiple studies to consider the relationship between different datasets. We give the efficient algorithms and the theoretical property for our methods. Furthermore, we apply our methods to the simulation data and three publicly available lung cancer gene expression datasets, and compare the performance with state-of-the-art methods. Our methods have good performance in simulation studies, and the analysis results on the three publicly available lung cancer gene expression datasets are clinically meaningful. Our methods can also be extended to other areas where datasets are heterogeneous. Nature Publishing Group UK 2020-04-01 /pmc/articles/PMC7113298/ /pubmed/32238826 http://dx.doi.org/10.1038/s41598-020-62473-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhang, Hui
Li, Shou-Jiang
Zhang, Hai
Yang, Zi-Yi
Ren, Yan-Qiong
Xia, Liang-Yong
Liang, Yong
Meta-Analysis Based on Nonconvex Regularization
title Meta-Analysis Based on Nonconvex Regularization
title_full Meta-Analysis Based on Nonconvex Regularization
title_fullStr Meta-Analysis Based on Nonconvex Regularization
title_full_unstemmed Meta-Analysis Based on Nonconvex Regularization
title_short Meta-Analysis Based on Nonconvex Regularization
title_sort meta-analysis based on nonconvex regularization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113298/
https://www.ncbi.nlm.nih.gov/pubmed/32238826
http://dx.doi.org/10.1038/s41598-020-62473-2
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