ArticleNucleic acids research2017
Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 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
- Distilling Direct Effects via Conditional Differential Gene Expression Analysis.bioRxiv : the preprint server for biology · 2025Article
- Robust and adaptive non-parametric tests for detecting general distributional shifts in gene expression.Cell reports methods · 2025Article
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.NPJ systems biology and applications · 2025Article
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- Quantifying transcriptome diversity: a review.Briefings in functional genomics · 2024Review
- Gene coexpression networks reveal a broad role for lncRNAs in inflammatory bowel disease.JCI insight · 2024Article
- Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging.Genome biology · 2023Article
- Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data.Genome biology · 2023Article
- 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
- Captivity induces large and population-dependent brain transcriptomic changes in wild-caught cane toads (Rhinella marina).Molecular ecology · 2022Article
- The pseudogenes of eukaryotic translation elongation factors (EEFs): Role in cancer and other human diseases.Genes & diseases · 2022Review
- Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.NAR genomics and bioinformatics · 2022Article
- Brain transcriptome analysis reveals gene expression differences associated with dispersal behaviour between range-front and range-core populations of invasive cane toads in Australia.Molecular ecology · 2022Article
- Developmental Programming: Prenatal Testosterone Excess on Liver and Muscle Coding and Noncoding RNA in Female Sheep.Endocrinology · 2022Article
- EHMT2 suppresses the variation of transcriptional switches in the mouse embryo.PLoS genetics · 2021Article
- Coordinated analysis of exon and intron data reveals novel differential gene expression changes.Scientific reports · 2020Article
- Predictive features of gene expression variation reveal mechanistic link with differential expression.Molecular systems biology · 2020Article
- Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility.Disease models & mechanisms · 2020Review
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2 authors.
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Abstract
Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion. We address challenges of analyzing large-scale RNA-seq data via several new developments to provide a comprehensive toolset that models technical excess zeros, identifies outliers efficiently, and evaluates differential expressions at biologically interesting levels. We evaluated performances of the MDSeq using simulated data when the ground truths are known. Results suggest that the MDSeq often outperforms current methods for the analysis of gene expression mean and variability. Moreover, the MDSeq is applied in two real RNA-seq studies, in which we identified functionally relevant genes and gene pathways. Specifically, the analysis of gene expression variability with the MDSeq on the GTEx human brain tissue data has identified pathways associated with common neurodegenerative disorders when gene expression means were conserved.
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