ArticleNAR genomics and bioinformatics2022
Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.
Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- SIEVEseq: unified differential expression, variability, and skewness analyses using RNA-Seq data.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- Bayesian Decision-Making Shapes Phenotypic Landscapes from Differentiation to Cancer.Entropy (Basel, Switzerland) · 2026Article
- Powerful gene network enrichment analysis and its application to severe COVID-19 gene network.Briefings in bioinformatics · 2025Article
- Robust and adaptive non-parametric tests for detecting general distributional shifts in gene expression.Cell reports methods · 2025Article
- Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression.PLoS computational biology · 2023Article
- clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution.PeerJ · 2023Article
- Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data.Computational and structural biotechnology journal · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.