ArticleStatistical methods in medical research2013
Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data.
Article in Statistical methods in medical research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 214 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
214 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of potential blood biomarkers for early diagnosis of Alzheimer's disease through RNA sequencing analysis.Alzheimer's research & therapy · 2020Pooled it
- MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes.Nature communications · 2017Trial
- 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
- Complementary ROC-Derived Indices for Screening Improper Expression Profiles in RNA-Seq Differential Expression Analysis.Balkan medical journal · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Metabolic profiling of therapy-induced senescent cancer cells via TPEF, MALDI-MS, and RNA-sequencing.Scientific reports · 2025Article
- From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods.Human genomics · 2025Review
- DGEAR: a web-based application for differential gene expression analysis and downstream functional insights.Functional & integrative genomics · 2025Article
- Distilling Direct Effects via Conditional Differential Gene Expression Analysis.bioRxiv : the preprint server for biology · 2025Article
- Transcriptional dynamics during karyogamy in rice zygotes.Development (Cambridge, England) · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Investigation the role of SIRT3, SIRT7, NFATC1, and PDL-1 genes in androgenetic alopecia.BMC research notes · 2024Article
- PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals.Cell reports methods · 2024Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer.Biochemical genetics · 2024Article
- Detecting differential transcript usage in complex diseases with SPIT.Cell reports methods · 2024Article
- Historical perspective and future directions: computational science in immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- Article
- SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.Genome biology · 2023Article
- Detecting differential transcript usage in complex diseases with SPIT.bioRxiv : the preprint server for biology · 2023Article
154 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We discuss the identification of features that are associated with an outcome in RNA-Sequencing (RNA-Seq) and other sequencing-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or 'sequencing depths'. Existing methods for this problem are based on Poisson or negative binomial models: they are useful but can be heavily influenced by 'outliers' in the data. We introduce a simple, non-parametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class or multiple-class outcomes. We compare our proposed method to Poisson and negative binomial-based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.