PLO(SC)²: Plots and Scripts for scRNA-seq analysis

<strong>Availability</strong> The PLOSC-project is available from https://github.com/mjoppich/PLOSC . The sequencing data (h5-files) were taken from: Pekayvaz K, Leunig A, Kaiser R, Joppich M, Brambs S, Janjic A, et al. Protective immune trajectories in early<br> viral containment of non-pneumonic SARS-CoV-2 infection. Nature communications. 2022 Feb;13(1):1018.<br> Available from: https://www.nature.com/articles/s41467-022-28508-0. <strong>Background</strong><br> scRNA-seq analysis has become a standard technique to study biological systems.<br> With decreasing costs for scRNA-seq experiments, these also become increasingly complex.<br> While the typical scRNA-seq analysis frameworks provide functionalities for the analysis of even such data sets, the required steps to follow for such experiments become complicated.<br> Moreover, default plots are not suitable to provide specific insight into such complex data sets, and should be enhanced, such that camera-ready fully-interpretable plots are provided. <strong>Results</strong><br> We thus describe here a collection of plotting and analysis scripts for use in Seurat-based scRNA-seq data analyses.<br> We first provide a collection of script blocks which allows for an easy basic analysis of scRNA-seq from Seurat object creation, filtering, and over data set integration in less than 10 steps.<br> Subsequently, we provide code blocks for the easy differential analysis of the obtained data sets, including visualizations.<br> Finally, several visualizations enhancing the functionalities of scRNA-seq analysis frameworks are presented, such as the enhanced Heatmap and DotPlot.<br> These, particularly, allow the user to specify how the shown values should be scaled, allowing the creation of condition-wise plots. <strong>Conclusions</strong><br> With the PLO(SC)² framework the data analysis of scRNA-seq experiments becomes more stream-lined, and visualizations for interpreting complex datasets are provided.<br> The PLO(SC)² scripts are available from GitHub, including a notebook showing how PLO(SC)² is applied on the use-case data presented here. This way, fellow researchers can directly apply the methods on their data.