DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression

Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential...

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Vydané v:PloS one Ročník 8; číslo 11; s. e79729
Hlavní autori: Yang, Jing, Yu, Hui, Liu, Bao-Hong, Zhao, Zhongming, Liu, Lei, Ma, Liang-Xiao, Li, Yi-Xue, Li, Yuan-Yuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 20.11.2013
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ISSN:1932-6203, 1932-6203
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Abstract Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.
AbstractList Motivation To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical.
Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner.To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject.With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical.DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.
Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner.MOTIVATIONDifferential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner.To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject.RESULTSTo advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject.With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical.CONCLUSIONSWith ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical.DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.AVAILABILITYDCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.
Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. Results To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named “Differential Regulation Analysis” (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. Conclusions With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. Availability DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.
Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.
Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. Results To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. Conclusions With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. Availability DCGL v2.0 package is available at
Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. Results To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named “Differential Regulation Analysis” (DRA) was designed, which consists of three major functions: DRsort , DRplot , and DRrank . DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. Conclusions With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. Availability DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.
Audience Academic
Author Yu, Hui
Li, Yuan-Yuan
Ma, Liang-Xiao
Yang, Jing
Zhao, Zhongming
Li, Yi-Xue
Liu, Lei
Liu, Bao-Hong
AuthorAffiliation 4 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
University of California, Los Angeles, United States of America
1 School of Biotechnology, East China University of Science and Technology, Shanghai, P. R. China
5 Departments of Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
3 Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai, P. R. China
2 Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China
6 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
AuthorAffiliation_xml – name: 3 Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai, P. R. China
– name: 2 Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China
– name: 5 Departments of Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
– name: 1 School of Biotechnology, East China University of Science and Technology, Shanghai, P. R. China
– name: 6 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
– name: University of California, Los Angeles, United States of America
– name: 4 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
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  givenname: Jing
  surname: Yang
  fullname: Yang, Jing
– sequence: 2
  givenname: Hui
  surname: Yu
  fullname: Yu, Hui
– sequence: 3
  givenname: Bao-Hong
  surname: Liu
  fullname: Liu, Bao-Hong
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  givenname: Zhongming
  surname: Zhao
  fullname: Zhao, Zhongming
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  givenname: Lei
  surname: Liu
  fullname: Liu, Lei
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  givenname: Liang-Xiao
  surname: Ma
  fullname: Ma, Liang-Xiao
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  givenname: Yi-Xue
  surname: Li
  fullname: Li, Yi-Xue
– sequence: 8
  givenname: Yuan-Yuan
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/24278165$$D View this record in MEDLINE/PubMed
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– notice: 2013 Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/3.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Conceived and designed the experiments: YYL HY JY YXL. Performed the experiments: JY HY BHL. Analyzed the data: JY HY. Contributed reagents/materials/analysis tools: YYL JY HY LXM LL. Wrote the paper: YYL HY JY ZZ.
Competing Interests: The authors have declared that no competing interests exist.
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Snippet Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies...
Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most...
Motivation To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named "Differential Regulation Analysis" (DRA) was...
Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most...
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SubjectTerms Algorithms
Analysis
Bioinformatics
Biology
Cell cycle
Disease
DNA methylation
Gene expression
Gene Regulatory Networks
Genes
Hepatitis
HyperText Markup Language
Informatics
Java (programming language)
Kinases
Laboratories
Links
Liver cancer
Medical research
Medicine
Oligonucleotide Array Sequence Analysis
Regulations
Researchers
Servers
Software
Studies
Transcription (Genetics)
Transcription factors
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