ArticleGenome biology2015
DGEclust: differential expression analysis of clustered count data.
Article in Genome biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data.MethodsX · 2025Article
- Article
- Article
- A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data.Bioinformatics (Oxford, England) · 2021Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
- A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs.Medicina (Kaunas, Lithuania) · 2019Article
- A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies.Entropy (Basel, Switzerland) · 2019Article
- Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data.Bioinformatics (Oxford, England) · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a statistical methodology, DGEclust, for differential expression analysis of digital expression data. Our method treats differential expression as a form of clustering, thus unifying these two concepts. Furthermore, it simultaneously addresses the problem of how many clusters are supported by the data and uncertainty in parameter estimation. DGEclust successfully identifies differentially expressed genes under a number of different scenarios, maintaining a low error rate and an excellent control of its false discovery rate with reasonable computational requirements. It is formulated to perform particularly well on low-replicated data and be applicable to multi-group data. DGEclust is available at http://dvav.github.io/dgeclust/.
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Registered trials
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