ArticleBMC bioinformatics2020
Negative binomial additive model for RNA-Seq data analysis.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Landscape transcriptomics reveals ecological constraints on fish under anthropogenic coastal change.Nature communications · 2026Article
- USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.Briefings in bioinformatics · 2026Article
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- Socioeconomic drivers of human Brucellosis in Ningxia, China: A one health and spatiotemporal analysis for targeted intervention.PLoS neglected tropical diseases · 2026Article
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
- Utilising Human Myometrial and Uterine Fibroid Stem Cell-Derived Three Dimentional Organoids as a Robust Model System for Understanding the Pathophysiology of Uterine Fibroids.Cell proliferation · 2025Article
- AI-Enhanced Fluorescein Angiography Detection of Diabetes-Induced Silent Retinal Capillary Dropout and RNA-Seq Identification of Pre-Symptomatic Biomarkers.Biomedicines · 2025Article
- A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies.Bioinformatics (Oxford, England) · 2024Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging.Human genomics · 2023Article
- Microenvironmental ammonia enhances T cell exhaustion in colorectal cancer.Cell metabolism · 2023Article
- Selenium-associated differentially expressed microRNAs and their targeted mRNAs across the placental genome in two U.S. birth cohorts.Epigenetics · 2022Observational
- Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime.Bioinformatics (Oxford, England) · 2022Article
- The placenta epigenome-brain axis: placental epigenomic and transcriptomic responses that preprogram cognitive impairment.Epigenomics · 2022Article
- OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.NAR genomics and bioinformatics · 2022Article
- Reuterin in the healthy gut microbiome suppresses colorectal cancer growth through altering redox balance.Cancer cell · 2022Article
- Sequences to Differences in Gene Expression: Analysis of RNA-Seq Data.Methods in molecular biology (Clifton, N.J.) · 2022Review
- PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.Genome biology · 2021Article
- Probabilistic outlier identification for RNA sequencing generalized linear models.NAR genomics and bioinformatics · 2021Article
- Naught all zeros in sequence count data are the same.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundHigh-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes.
resultsWe introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes.
conclusionsBased on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.
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