ArticlePloS one2015
CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.Frontiers in neuroscience · 2025Article
- XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.PLoS computational biology · 2023Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer.Genome biology · 2019Article
- Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data.PLoS computational biology · 2019Article
- BioNetStat: A Tool for Biological Networks Differential Analysis.Frontiers in genetics · 2019Article
- A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.BMC systems biology · 2018Article
- Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis.Frontiers in physiology · 2018Article
- Expression of the Circadian Clock GeneFrontiers in oncology · 2018Article
- Investigation of coordination and order in transcription regulation of innate and adaptive immunity genes in type 1 diabetes.BMC medical genomics · 2017Article
- GSAR: Bioconductor package for Gene Set analysis in R.BMC bioinformatics · 2017Article
- Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Deletion and low expression of NFKBIA are associated with poor prognosis in lower-grade glioma patients.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially associated genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their "importance" in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed.
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