Introduction

Gene co-expression network analysis from transcriptomic studies can elucidate gene-gene interactions and regulatory mechanisms. Differential co-expression analysis helps further detect alterations of regulatory activities in case/control comparison. Co-expression networks estimated from single transcriptomic study is often unstable and not generalizable due to cohort bias and limited sample size. With the rapid accumulation of publicly available transcriptomic studies, co-expression analysis combining multiple transcriptomic studies can provide more accurate and robust results.In this paper, we propose a meta-analytic framework for detecting differentially co-expressed networks (MetaDCN). Differentially co-expressed seed modules are first detected by optimizing an energy function via simulated annealing. Basic modules sharing common pathways are merged into pathway-centric supermodules and a Cytoscape plug-in (MetaDCNExplorer) is developed to visualize and explore the findings. We applied MetaDCN to two breast cancer applications: ER+/ER- comparison using five training and three testing studies, and ILC/IDC comparison with two training and two testing studies. We identified 20 and 4 supermodules for ER+/ER- and ILC/IDC comparisons, respectively. Ranking atop are 'immune response pathway' and 'complement cascades pathway' for ER comparison, and 'extracellular matrix pathway' for ILC/IDC comparison. Without the need for prior information, the results from MetaDCN confirm existing as well as discover novel disease mechanisms in a systems manner.R package 'MetaDCN' and Cytoscape App 'MetaDCNExplorer' are available at http://tsenglab.biostat.pitt.edu/software.htm .ctseng@pitt.edu.Supplementary data are available at Bioinformatics online.

Publications

  1. MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.
    Cite this
    Zhu L, Ding Y, Chen CY, Wang L, Huo Z, Kim S, Sotiriou C, Oesterreich S, Tseng GC, 2017-04-01 - Bioinformatics (Oxford, England)

Credits

  1. Li Zhu
    Developer

    Department of Biostatistics, Graduate School of Public Health, United States of America

  2. Ying Ding
    Developer

    Department of Computational and Systems Biology, School of Medicine, United States of America

  3. Cho-Yi Chen
    Developer

    Genome and Systems Biology Degree Program, National Taiwan University, Taiwan, Province of China

  4. Lin Wang
    Developer

    Department of Biostatistics, Graduate School of Public Health, United States of America

  5. Zhiguang Huo
    Developer

    Department of Biostatistics, Graduate School of Public Health, United States of America

  6. SungHwan Kim
    Developer

    Department of Biostatistics, Graduate School of Public Health, United States of America

  7. Christos Sotiriou
    Developer

    Breast Cancer Translational Research Laboratory, J. C. Heuson, Belgium

  8. Steffi Oesterreich
    Developer

    Magee-Women's Research Institute, Pittsburgh, United States of America

  9. George C Tseng
    Investigator

    Department of Computational and Systems Biology, School of Medicine, United States of America

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Summary
AccessionBT001620
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByGeorge C Tseng