ArticleBioinformatics (Oxford, England)2018
AEGS: identifying aberrantly expressed gene sets for differential variability analysis.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Network-based exploration of 4-(phenylsulfonyl)morpholine molecules for metastatic triple-negative breast cancer suppression.PLoS computational biology · 2026Article
- Overdispersed gene expression in schizophrenia.NPJ schizophrenia · 2020Article
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Authors and funding
5 authors.
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Abstract
Motivation: In gene expression studies, differential expression (DE) analysis has been widely used to identify genes with shifted expression mean between groups. Recently, differential variability (DV) analysis has been increasingly applied as analyzing changed expression variability (e.g. the changes in expression variance) between groups may reveal underlying genetic heterogeneity and undetected interactions, which has great implications in many fields of biology. An easy-to-use tool for DV analysis is needed. Results: We develop AEGS for DV analysis, to identify aberrantly expressed gene sets in diseased cases but not in controls. AEGS can rank individual genes in an aberrantly expressed gene set by each gene's relative contribution to the total degree of aberrant expression, prioritizing top genes. AEGS can be used for discovering gene sets with disease-specific expression variability changes. Availability and implementation: AEGS web server is accessible at http://bmi.xmu.edu.cn:8003/AEGS, where a stand-alone AEGS application can also be downloaded. Contact: glji@xmu.edu.cn.
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