title: MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.
abstract: BACKGROUND
  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.
  
  
  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.
  
  
  METHODS
  R package 'MetaDCN' and Cytoscape App 'MetaDCNExplorer' are available at http://tsenglab.biostat.pitt.edu/software.htm .
  
  
  BACKGROUND
  ctseng@pitt.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Zhu
authors.affiliation: Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ding
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Genome and Systems Biology Degree Program, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Cho-Yi
authors.initials: CY
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
authors.forename: Zhiguang
authors.initials: Z
authors.lastname: Huo
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
authors.forename: SungHwan
authors.initials: S
authors.lastname: Kim
authors.affiliation: Breast Cancer Translational Research Laboratory, J. C. Heuson, Institut Jules Bordet, University Libre de Bruxelles, Brussels 1000, Belgium.
authors.forename: Christos
authors.initials: C
authors.lastname: Sotiriou
authors.affiliation: Magee-Women's Research Institute, Pittsburgh, PA 15213, USA.
authors.forename: Steffi
authors.initials: S
authors.lastname: Oesterreich
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
authors.forename: George C
authors.initials: GC
authors.lastname: Tseng
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D052138:Genes, Neoplasm; D006801:Humans; D011960:Receptors, Estrogen / Q000378:metabolism; D018401:Sample Size
chemical_list: D011960:Receptors, Estrogen