Binarized single cell RNA sequencing data clustering

Abstract

Single-cell RNA sequencing data clustering is a valuable technique for demonstrating cell-to-cell heterogeneity and revealing cell dynamics within and amongst groups. Large up-scaling of scRNA-seq datasets in recent years pose computational challenges for existing state-of-the-art clustering techniques. A possible solution to tackle these challenges is to binarize the scRNA-seq data and perform clustering using optimized binary methods. Using a binary clustering pipeline we demonstrate that binary clustering solutions resemble conventional clustering solutions for large clusters, but show less resemblance for smaller clusters. We also show that the Leiden community detection algorithm can achieve higher cluster quality compared to the Louvain algorithm for the binarized data.