ArticleGigaByte (Hong Kong, China)2025
Chevreul: an R bioconductor package for exploratory analysis of full-length single cell sequencing.
Article in GigaByte (Hong Kong, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- Chevreul: an R bioconductor package for exploratory analysis of full-length single cell sequencing.GigaByte (Hong Kong, China) · 2025Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
Abstract
Chevreul is an open-source R Bioconductor package and interactive R Shiny app for processing and visualising single-cell RNA sequencing (scRNA-seq) data. Chevreul differs from other scRNA-seq analysis packages in its ease of use, capacity to analyze full-length RNA sequencing data for exon coverage and transcript isoform inference, and support for batch correction. Chevreul enables exploratory analyses of scRNA-seq data using Bioconductor SingleCellExperiment objects (or converted Seurat objects), including batch integration, quality control filtering, read count normalization and transformation, dimensionality reduction, clustering at a range of resolutions, and cluster marker gene identification. Processed data can be visualized in the R Shiny app. Gene or transcript expression can be visualized using PCA, tSNE, UMAP, heatmaps, or violin plots; differential expression can be evaluated with several statistical tests. Chevreul also provides accessible tools for isoform-level analyses and alternative splicing detection. Chevreul empowers researchers without programming experience to analyze full-length scRNA-seq data. Availability & implementation: Chevreul is implemented in R, and the R package and integrated Shiny application are freely available at https://github.com/cobriniklab/chevreul with constituent packages hosted on Bioconductor at https://bioconductor.org/packages/chevreulProcess, https://bioconductor.org/packages/chevreulPlot, and https://bioconductor.org/packages/chevreulShiny.
Identifiers
What OpenQuestion holds
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.