Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.
Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measure...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3925093?pdf=render |
| _version_ | 1811232647648641024 |
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| author | Xiaobo Guo Ye Zhang Wenhao Hu Haizhu Tan Xueqin Wang |
| author_facet | Xiaobo Guo Ye Zhang Wenhao Hu Haizhu Tan Xueqin Wang |
| author_sort | Xiaobo Guo |
| collection | DOAJ |
| description | Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference. |
| first_indexed | 2024-04-12T11:06:29Z |
| format | Article |
| id | doaj.art-2c33e4f6ab9a4172864b193e09cacae0 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T11:06:29Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2c33e4f6ab9a4172864b193e09cacae02022-12-22T03:35:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8744610.1371/journal.pone.0087446Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.Xiaobo GuoYe ZhangWenhao HuHaizhu TanXueqin WangNonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.http://europepmc.org/articles/PMC3925093?pdf=render |
| spellingShingle | Xiaobo Guo Ye Zhang Wenhao Hu Haizhu Tan Xueqin Wang Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation. PLoS ONE |
| title | Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation. |
| title_full | Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation. |
| title_fullStr | Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation. |
| title_full_unstemmed | Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation. |
| title_short | Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation. |
| title_sort | inferring nonlinear gene regulatory networks from gene expression data based on distance correlation |
| url | http://europepmc.org/articles/PMC3925093?pdf=render |
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