ArticleBMC bioinformatics2021
Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data.
Article in BMC bioinformatics, 2021. 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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Who cites it
10 citing papers in PubMed.
- Combining Phytochemical and Computational Approach on Citrus hystrix DC. (Kaffir Lime) Peel Extract: Anti-Diabetic and Anti-Inflammatory Activities.Chemistry & biodiversity · 2026Article
- Deregulated miR-145 and miR-27b in Hutchinson-Gilford progeria syndrome: implications for adipogenesis.Aging · 2025Article
- Role of Endoplasmic Reticulum Stress-Associated Genes in Septic Neonatal Foals.Antioxidants (Basel, Switzerland) · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.Nucleic acids research · 2025Article
- MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data.MethodsX · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.bioRxiv : the preprint server for biology · 2025Article
- Article
- Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.Briefings in bioinformatics · 2024Article
- Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.Advanced genetics (Hoboken, N.J.) · 2023Review
- Molecular docking and proteomics reveals the synergistic antibacterial mechanism of theaflavin with β-lactam antibiotics against MRSA.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundRNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.
resultsThe input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (P
conclusionsMBCdeg with DEGES normalization can be used in the identification of DEGs when the P
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