ArticleBMC bioinformatics2019
Visualization methods for differential expression analysis.
Article in BMC bioinformatics, 2019. 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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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
11 citing papers in PubMed.
- Analysis and visualization of expression patterns with fuzzy sets as FlowSets.NAR genomics and bioinformatics · 2026Article
- SeqExpressionAnalyser: An R Package for Automated End-to-End RNA-Seq Analysis From Reads to Differential Expression.Bioinformatics and biology insights · 2026Article
- Deep Immune and RNA Profiling Revealed Distinct Circulating CD163+ Monocytes in Diabetes-Related Complications.International journal of molecular sciences · 2024Article
- Functional transcriptome analysis revealed major changes in pathways affecting systems biology of Tharparkar cattle under seasonal heat stress.3 Biotech · 2024Article
- Decoding the role of oxidative stress resistance and alternative carbon substrate assimilation in the mature biofilm growth mode of Candida glabrata.BMC microbiology · 2024Article
- Regulatory Pathways in Growth Plate Chondrocytes that Are Impacted by Matrix Vesicle microRNA Identified by Targeted RISC Pulldown and Sequencing of the Resulting Transcriptome.Calcified tissue international · 2024Article
- GmGLU1 and GmRR4 contribute to iron deficiency tolerance in soybean.Frontiers in plant science · 2024Article
- 1α,25-Dihydroxyvitamin DCalcified tissue international · 2023Article
- Transforming growth factor beta signaling and decidual integrity in mice†.Biology of reproduction · 2020Article
- bigPint: A Bioconductor visualization package that makes big data pint-sized.PLoS computational biology · 2020Article
- Examining Short-Term Responses to a Long-Term Problem: RNA-Seq Analyses of Iron Deficiency Chlorosis Tolerant Soybean.International journal of molecular sciences · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundDespite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users.
resultsIn this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package "bigPint" includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The "bigPint" website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code.
conclusionsWe emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.
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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.