ArticleBMC bioinformatics2019
DEvis: an R package for aggregation and visualization of differential expression data.
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 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
13 citing papers in PubMed.
- Affect Expression During Social and Non-Social Contexts in Autistic Young Adults.Journal of autism and developmental disorders · 2026Article
- MultiModalGraphics: an R package for graphical integration of multi-omics datasets.BMC bioinformatics · 2025Article
- Temporal regulation of gene expression during auxin-triggered crown root formation in barley: an integrated approach.Plant & cell physiology · 2025Article
- VIBE: an R-package for VIsualization of Bulk RNA Expression data for therapeutic targeting and disease stratification.Frontiers in oncology · 2024Article
- Time-course transcriptome analysis of lungs from mice infected with inhaled aerosolizedJournal of thoracic disease · 2023Article
- Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.JMIR medical informatics · 2022Article
- Multidimensional analysis of the host response reveals prognostic and pathogen-driven immune subtypes among adults with sepsis in Uganda.Critical care (London, England) · 2022Observational
- Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready withNAR genomics and bioinformatics · 2021Article
- Article
- ideal: an R/Bioconductor package for interactive differential expression analysis.BMC bioinformatics · 2020Article
- MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets.Nucleic acids research · 2020Article
- Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes.Cell reports · 2020Article
- Dynamic Polarization of Rab11a Modulates Crb2a Localization and Impacts Signaling to Regulate Retinal Neurogenesis.Frontiers in cell and developmental biology · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundExisting tools for the aggregation and visualization of differential expression data have discrete functionality and require that end-users rely on multiple software packages with complex dependencies or manually manipulate data for analysis and interpretation. Furthermore, at present, data aggregation and visualization are laborious, time consuming, and subject to human error. This is a serious limitation on the current state of differential transcriptomic analysis, which makes it necessary to expend extensive time and resources to reach the point where biological meaning can be interpreted. Such an approach for analysis also leads to scattered and non-standardized code, unsystematic project management and non-reproducible result sets.
resultsHere, we present a differential expression analysis toolkit, DEvis, that provides a powerful, integrated solution for the analysis of differential expression data with a rapid turnaround time. DEvis has simple installation requirements and provides a convenient, user-friendly R package that addresses the issues inherent to complex multi-factor experiments, such as multiple contrast aggregation and integration, result sorting and selection, visualization, project management, and reproducibility. This tool increases the capabilities of differential expression analysis while reducing workload and the potential for manual error. Furthermore, it provides a much-needed encapsulation of scattered functionality, making large and complex analysis more efficient and reproducible.
conclusionDEvis provides a wide range of powerful visualization, data aggregation, and project management tools that provide flexibility and speed in analysis. The functionality provided by DEVis increases efficiency of analysis and supplies researchers with new and relevant means for the analysis of large and complicated transcriptomic experiments. DEvis furthermore incorporates automatic project management capabilities, which standardizes analysis and ensures the reproducibility of results. After the establishment of statistical frameworks that identify differentially expressed genes, this package is the next logical step for differential transcriptomic analysis, establishing the critical framework necessary to manipulate, explore, and extract biologically relevant meaning from differential expression data.
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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.