Incorporating Practical Single Cell and Spatial Transcriptomics Analysis in a Bioinformatics Course

Single-cell RNA sequencing (scRNAseq) and spatial transcriptomics have emerged as a powerful tool for unraveling the heterogeneity of cell populations and understanding cellular dynamics at unprecedented resolution. However, analyzing scRNAseq and spatial data poses unique challenges due to their high dimensionality and inherent noise. In this paper, a comprehensive tutorial on incorporating practical single-cell and spatial transcriptomics analysis is presented using Scanpy in a bioinformatics course at the University of Belgrade, School of Electrical Engineering. Scanpy, a versatile Python toolkit, offers a wide range of functionalities for quality control, dimensionality reduction, clustering, and trajectory inference. Through lectures and hands-on exercises, students learn essential concepts and techniques, including working with annotated data objects, preprocessing scRNAseq data, performing dimensionality reduction using Principal Component Analysis and Uniform Manifold Approximation and Projection, conducting clustering analysis with the Leiden algorithm, and identifying marker genes for cell type annotation. By equipping students with these skills, we aim to empower the next generation of bioinformaticians to explore the complexities of single-cell and spatial transcriptomics data and advance our understanding of cellular biology.