SC3s - efficient scaling of single cell consensus clustering to millions of cells

Technological advances have paved the way for single cell RNAseq (scRNAseq) datasets containing several million cells 1 . Such large datasets require highly efficient algorithms to enable analyses at reasonable times and hardware requirements 2 . A crucial step in single cell workflows is unsupervised clustering, which aims to delineate putative cell types or cell states based on transcriptional similarity 3 . Here, we present a highly efficient k-means based approach, and we demonstrate that it scales linearly with the number of cells with regards to time and memory.