ArticleFrontiers in genetics2021
Comparing Statistical Tests for Differential Network Analysis of Gene Modules.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Efficient differential latent network analysis: applications to colon cancer.BMC medical genomics · 2026Article
- Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease.BMC genomics · 2024Article
- Transcriptomic Insight into the Pollen Tube Growth ofPlants (Basel, Switzerland) · 2023Article
- MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.Briefings in bioinformatics · 2023Article
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5 authors.
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Abstract
Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a
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