ArticleBMC genomics2017
Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ARTP Mutagenesis ofJournal of microbiology and biotechnology · 2025Article
- Bdelloid rotifers deploy horizontally acquired biosynthetic genes against a fungal pathogen.Nature communications · 2024Article
- Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis.Saudi journal of biological sciences · 2023Article
- Simultaneous co-infection with swine influenza A and porcine reproductive and respiratory syndrome viruses potentiates adaptive immune responses.Frontiers in immunology · 2023Article
- Article
- Epitranscriptomics of Ischemic Heart Disease-The IHD-EPITRAN Study Design and Objectives.International journal of molecular sciences · 2021Article
- Regulation of gene expression in the bovine blastocyst by colony-stimulating factor 2 is disrupted by CRISPR/Cas9-mediated deletion of CSF2RA.Biology of reproduction · 2021Article
- Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data.PloS one · 2020Article
- Evidence against tetrapod-wide digit identities and for a limited frame shift in bird wings.Nature communications · 2019Article
- GScluster: network-weighted gene-set clustering analysis.BMC genomics · 2019Article
- The relationship between case-control differential gene expression from brain tissue and genetic associations in schizophrenia.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric GeneticsArticle
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2 authors.
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Abstract
backgroundIn differential expression analysis of RNA-sequencing (RNA-seq) read count data for two sample groups, it is known that highly expressed genes (or longer genes) are more likely to be differentially expressed which is called read count bias (or gene length bias). This bias had great effect on the downstream Gene Ontology over-representation analysis. However, such a bias has not been systematically analyzed for different replicate types of RNA-seq data.
resultsWe show that the dispersion coefficient of a gene in the negative binomial modeling of read counts is the critical determinant of the read count bias (and gene length bias) by mathematical inference and tests for a number of simulated and real RNA-seq datasets. We demonstrate that the read count bias is mostly confined to data with small gene dispersions (e.g., technical replicates and some of genetically identical replicates such as cell lines or inbred animals), and many biological replicate data from unrelated samples do not suffer from such a bias except for genes with some small counts. It is also shown that the sample-permuting GSEA method yields a considerable number of false positives caused by the read count bias, while the preranked method does not.
conclusionWe showed the small gene variance (similarly, dispersion) is the main cause of read count bias (and gene length bias) for the first time and analyzed the read count bias for different replicate types of RNA-seq data and its effect on gene-set enrichment analysis.
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