Availability 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 viral containment of non-pneumonic SARS-CoV-2 infection. Nature communications. 2022 Feb;13(1):1018. Available from: https://www.nature.com/articles/s41467-022-28508-0. Background scRNA-seq analysis has become a standard technique to study biological systems. With decreasing costs for scRNA-seq experiments, these also become increasingly complex. 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. 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. Results We thus describe here a collection of plotting and analysis scripts for use in Seurat-based scRNA-seq data analyses. 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. Subsequently, we provide code blocks for the easy differential analysis of the obtained data sets, including visualizations. Finally, several visualizations enhancing the functionalities of scRNA-seq analysis frameworks are presented, such as the enhanced Heatmap and DotPlot. These, particularly, allow the user to specify how the shown values should be scaled, allowing the creation of condition-wise plots. Conclusions 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. 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.