On Clustering scRNA-seq Data

Explore the challenges and strategies in clustering single-cell RNA sequencing (scRNA-seq) data through this insightful 53-minute talk by Živa Urbančič from the Applied Algebraic Topology Network. Delve into the complexities of analyzing gene expression profiles at the individual cell level, understanding the impact of biological variability and technical artifacts on data interpretation. Learn about various clustering methods used to group cells into distinct types, with a focus on the innovative $k$-cluster approach developed by Bobrowski and Škraba. Witness the practical application of this new clustering method on a dataset of neurons in fruit flies, demonstrating its effectiveness in addressing the unique challenges posed by scRNA-seq data analysis.