Background scRNA-seq analysis has become a standard technique for studying biological systems. As costs decrease, scRNA-seq experiments become increasingly complex. While typical scRNA-seq analysis frameworks provide basic functionality to analyze such data sets, downstream analysis and visualization become a bottleneck. Standard plots are not always suitable to provide specific insight into such complex data sets and should be extended to provide camera-ready, meaningful plots.
Results With PLO(SC)², a collection of plotting and analysis scripts for use in Seurat-based scRNA-seq data analyses is presented, which are accessible for custom script-based analyses or within an R shiny app. The analysis scripts mainly provide a collection of code blocks which enable a comfortable basic analysis of scRNA-seq data from Seurat object creation, filtering, and over data set integration in less than 10 function calls. Subsequently, code blocks for performing differential and enrichment analyses and corresponding visualizations are provided. Finally, several enhanced visualizations are provided, such as the enhanced Heatmap, DotPlot and comparative Box-/Violin plots. These, particularly, allow the user to specify how the shown values should be scaled, allowing the accurate creation of condition-wise plots.
Conclusion With the PLO(SC)² framework data analysis of scRNA-seq experiments is performed more comfortable and stream-lined, while visualizations are enhanced to be suitable for interpreting complex datasets. The PLO(SC)² scripts are available from GitHub, including a notebook showing how PLO(SC)² is applied within a script-based analysis, and an R shiny app.
Before installing PLO(SC)², make sure the following Bioconductor dependencies are installed:
BiocManager::install(c("biomaRt", "clusterProfiler", "ReactomePA", "org.Hs.eg.db", "org.Mm.eg.db", "ComplexHeatmap", "enrichplot", "EnhancedVolcano"))
Then you can install PLO(SC)² using remotes:
remotes::install_github("mjoppich/PLOSC")