In order to reduce the impact of technical variation inherent in single-cell RNA sequencing (scRNA-seq) technologies on biological interpretation of experiments, rigorous preprocessing and quality control is required to transform raw sequencing reads into high-quality, gene and transcript counts. While hundreds of tools have been developed for this purpose, the vast majority of the most widely used tools are built for the R software environment. With an increasing number of new tools now being developed using Python, it is necessary to develop integrative workflows that leverage tools from both platforms. We have therefore developed, SASCRiP (Sequencing Analysis of Single-Cell RNA in Python), a modular single-cell preprocessing workflow that integrates functionality from existing, widely used R and Python packages, and additional custom features and visualizations, to enable preprocessing of scRNA-seq data derived from technologies that use unique molecular identifier (UMI) sequences in a single Python analysis workflow. We describe the utility of SASCRiP using datasets derived from peripheral blood mononuclear cells sequenced using droplet-based, 3′-end sequencing technology. We highlight SASCRiP’s diagnostic visualizations and fully customizable functions, and demonstrate how SASCRiP provides a highly flexible, integrative Python workflow for preparing unprocessed UMI count-based scRNA-seq data for subsequent downstream analyses. SASCRiP is freely available through PyPi or from the GitHub page.
