Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data Van Hoan Do, Francisca Rojas Ringeling, and Stefan Canzar

Do, Van Hoan; Ringeling, Francisca Rojas und Canzar, Stefan (2021): Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data Van Hoan Do, Francisca Rojas Ringeling, and Stefan Canzar. In: Genome Research, Bd. 31, Nr. 4: S. 677-688

Abstract

A fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultralarge scRNA-seq data sets produced by droplet-based sequencing technologies. Most existing methods rely on a sampling step to bridge the gap between algorithm scalability and volume of the data. Ignoring large parts of the data, however, often yields inaccurate groupings of cells and risks overlooking rare cell types. We propose method Specter that adopts and extends recent algorithmic advances in (fast) spectral clustering. In contrast to methods that cluster a (random) subsample of the data, we adopt the idea of landmarks that are used to create a sparse representation of the full data from which a spectral embedding can then be computed in linear time. We exploit Specter?s speed in a cluster ensemble scheme that achieves a substantial improvement in accuracy over existing methods and identifies rare cell types with high sensitivity. Its lineartime complexity allows Specter to scale to millions of cells and leads to fast computation times in practice. Furthermore, on CITE-seq data that simultaneously measures gene and protein marker expression, we show that Specter is able to use multimodal omics measurements to resolve subtle transcriptomic differences between subpopulations of cells. tion at which important questions in cell biology can be addressed. It has helped to identify novel cell types based on commonalities and differences in genome-wide expression patterns, reconstruct the heterogeneous composition of cell populations in tumors and their microenvironment, and unveil regulatory programs that govern the dynamic changes in gene expression along developmental trajectories. One of the most fundamental computational tasks in the context of scRNA-seq analysis is the identification of groups of cells that are similar in their expression patterns, that is, their tran

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Chemie und Pharmazie > Department Biochemie
• Themengebiete: 500 Naturwissenschaften und Mathematik > 540 Chemie
• ISSN: 1088-9051
• Sprache: Englisch