Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

Abstract

The rapid proliferation of single-cell RNA-Sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity have increased, most existing algorithms require significant user-tuning, are heavily reliant on dimensionality reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface. Here, we describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse-NMF, cluster \"fitness\", SVM) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from the Human Cell Atlas, we show that the PageRank algorithm effectively down samples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar distinct cell-types and while recovering novel transcriptionally unique cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets.\n\nHighlightsO_LIICGS2 outperforms alternative approaches in small and ultra-large benchmark datasets\nC_LIO_LIIntegrates multiple solutions for cell-type detection with supervised refinement\nC_LIO_LIScales effectively to resolve rare cell-states from ultra-large datasets using PageRank sampling with a low memory footprint\nC_LIO_LIIntegrated into AltAnalyze to enable sophisticated and automated downstream analysis\nC_LI