RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data.

Autor:	Schmidt F; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Ranjan B; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Lin QXX; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Krishnan V; DUKE-NUS Medical School, 8 College Rd, 169857, Singapore., Joanito I; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Honardoost MA; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore.; Department of Medicine, School of Medicine, National University of Singapore, 1 Kent Ridge Road, level 10, NUHS Tower Block, 119228, Singapore., Nawaz Z; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Venkatesh PN; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Tan J; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Rayan NA; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore., Ong ST; DUKE-NUS Medical School, 8 College Rd, 169857, Singapore.; Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA., Prabhakar S; Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis St, 138672, Singapore.
Jazyk:	angličtina
Zdroj:	Nucleic acids research [Nucleic Acids Res] 2021 Sep 07; Vol. 49 (15), pp. 8505-8519.
DOI:	10.1093/nar/gkab632
Abstrakt:	The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets. (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Databáze:	MEDLINE
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