Stachelek Kevin, Bhat Bhavana, Cobrinik David
The Vision Center, Department of Surgery, and Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Cancer Biology and Genomics Program, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
bioRxiv. 2025 Jun 1:2025.05.27.656486. doi: 10.1101/2025.05.27.656486.
Chevreul is an open-source R Bioconductor package and interactive R Shiny app for processing and visualization of single cell RNA sequencing (scRNA-seq) data. It differs from other scRNA-seq analysis packages in its ease of use, its capacity to analyze full-length RNA sequencing data for exon coverage and transcript isoform inference, and its support for batch correction. Chevreul enables exploratory analysis of scRNA-seq data using Bioconductor SingleCellExperiment or Seurat objects. Simple processing functions with sensible default settings enable 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 an interactive R Shiny app with dynamically linked plots. Expression of gene or transcript features can be displayed on PCA, tSNE, and UMAP embeddings, heatmaps, or violin plots while differential expression can be evaluated with several statistical tests without extensive programming. Existing analysis tools do not provide specialized tools for isoform-level analysis or alternative splicing detection. By enabling isoform-level expression analysis for differential expression, dimensionality reduction and batch integration, Chevreul empowers researchers without prior programming experience to analyze full-length scRNA-seq data.
Chevreul是一个用于处理和可视化单细胞RNA测序（scRNA-seq）数据的开源R语言生物导体包和交互式R Shiny应用程序。它与其他scRNA-seq分析包的不同之处在于其易用性、分析全长RNA测序数据以进行外显子覆盖和转录本异构体推断的能力以及对批次校正的支持。Chevreul能够使用生物导体SingleCellExperiment或Seurat对象对scRNA-seq数据进行探索性分析。具有合理默认设置的简单处理功能可实现批次整合、质量控制过滤、读取计数归一化和转换、降维、一系列分辨率下的聚类以及聚类标记基因识别。处理后的数据可以在具有动态链接图的交互式R Shiny应用程序中可视化。基因或转录本特征的表达可以显示在主成分分析（PCA）、t分布随机邻域嵌入（tSNE）和均匀流形近似与投影（UMAP）嵌入、热图或小提琴图上，同时可以通过几种统计测试评估差异表达，而无需大量编程。现有的分析工具没有提供用于异构体水平分析或可变剪接检测的专门工具。通过实现异构体水平的差异表达分析、降维和批次整合，Chevreul使没有编程经验的研究人员能够分析全长scRNA-seq数据。
