  Identifying cell clusters is a critical step for single-cell transcriptomics
study. Despite the numerous clustering tools developed recently, the rapid
growth of scRNA-seq volumes prompts for a more (computationally) efficient
clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral
clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite
graph representation algorithm, Secuer enjoys reduced runtime and memory usage
by orders of magnitude, especially for ultra-large datasets profiling over 1 or
even 10 million cells. Meanwhile, Secuer also achieves better or comparable
accuracy than competing methods in small and moderate benchmark datasets.
Furthermore, we showcase that Secuer can also serve as a building block for a
new consensus clustering method, Secuer-consensus, which again greatly improves
the runtime and scalability of state-of-the-art consensus clustering methods
while also maintaining the accuracy. Overall, Secuer is a versatile, accurate,
and scalable clustering framework suitable for small to ultra-large single-cell
clustering tasks.
