ArticleBMC bioinformatics2020
Data-based RNA-seq simulations by binomial thinning.
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 19 papers.
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Who cites it
19 citing papers in PubMed.
- Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data.PLoS computational biology · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2.NAR genomics and bioinformatics · 2025Article
- A comprehensive review and benchmark of differential analysis tools for Hi-C data.Briefings in bioinformatics · 2025Review
- Crafted experiments to evaluate feature selection methods for single-cell RNA-seq data.NAR genomics and bioinformatics · 2025Article
- Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition.Nature genetics · 2025Article
- RNAseqCovarImpute: a multiple imputation procedure that outperforms complete case and single imputation differential expression analysis.Genome biology · 2024Article
- Cell atlas of the regenerating human liver after portal vein embolization.Nature communications · 2024Article
- Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- Inference after latent variable estimation for single-cell RNA sequencing data.Biostatistics (Oxford, England) · 2023Article
- Sex-biased gene expression across mammalian organ development and evolution.Science (New York, N.Y.) · 2023Article
- Roastgsa: a comparison of rotation-based scores for gene set enrichment analysis.BMC bioinformatics · 2023Article
- ARBic: an all-round biclustering algorithm for analyzing gene expression data.NAR genomics and bioinformatics · 2023Article
- Resistant cumin cultivar, GC-4 countersFrontiers in plant science · 2023Article
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- Kidney transcriptome response to salinity adaptation inFrontiers in physiology · 2022Article
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance.
resultsRather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets.
conclusionsUsing data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff.
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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.