Manual cell selection in single cell transcriptomics using scSELpy supports the analysis of immune cell subsets

Abstract

Introduction: Single cell RNA sequencing plays an increasing and indispensable role in immunological research such as in the field of inflammatory bowel diseases (IBD). Professional pipelines are complex, but tools for the manual selection and further downstream analysis of single cell populations are missing so far. Methods: We developed a tool called scSELpy, which can easily be integrated into Scanpy-based pipelines, allowing the manual selection of cells on single cell transcriptomic datasets by drawing polygons on various data representations. The tool further supports the downstream analysis of the selected cells and the plotting of results. Results: Taking advantage of two previously published single cell RNA sequencing datasets we show that this tool is useful for the positive and negative selection of T cell subsets implicated in IBD beyond standard clustering. We further demonstrate the feasibility for subphenotyping T cell subsets and use scSELpy to corroborate earlier conclusions drawn from the dataset. Moreover, we also show its usefulness in the context of T cell receptor sequencing. Discussion: Collectively, scSELpy is a promising additive tool fulfilling a so far unmet need in the field of single cell transcriptomic analysis that might support future immunological research.