General Information
| Description | Meta-analysis framework for differential co-expression network detection with an application in breast cancer |
| Homepage | http://tsenglab.biostat.pitt.edu/software.htm |
Publications
MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer
Motivation:
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.
Availability and Implementation:
R package ‘MetaDCN’ and Cytoscape App ‘MetaDCNExplorer’ are available at http://tsenglab.biostat.pitt.edu/software.htm.
Contact:
ctseng@pitt.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
Related Tools
stringgaussnet
Published July 2015
From differentially expressed genes to semantic and Gaussian networks generation
MetaKTSP
Published March 2016
A meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis
NCMine
Published July 2016
Core-peripheral based functional module detection using near-clique mining
QUBIC
Published October 2016
A bioconductor package for qualitative biclustering analysis of gene co-expression data
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
