EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the measurement of transcriptomic profiles at the single-cell level. With the increasing application of scRNA-seq in larger-scale studies, the problem of appropriately clustering cells emerges when the scRNA-se...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) Vol. 38; no. 10; pp. 2692 - 2699
Main Authors: Wei, Xin, Li, Ziyi, Ji, Hongkai, Wu, Hao
Format: Journal Article
Language:English
Published: England Oxford University Press 13.05.2022
Subjects:
Algorithms
Cluster Analysis
Gene Expression Profiling - methods
Humans
Original Papers
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
ISSN:1367-4803, 1367-4811
Online Access:Get full text
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Summary:Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the measurement of transcriptomic profiles at the single-cell level. With the increasing application of scRNA-seq in larger-scale studies, the problem of appropriately clustering cells emerges when the scRNA-seq data are from multiple subjects. One challenge is the subject-specific variation; systematic heterogeneity from multiple subjects may have a significant impact on clustering accuracy. Existing methods seeking to address such effects suffer from several limitations. We develop a novel statistical method, EDClust, for multi-subject scRNA-seq cell clustering. EDClust models the sequence read counts by a mixture of Dirichlet-multinomial distributions and explicitly accounts for cell-type heterogeneity, subject heterogeneity and clustering uncertainty. An EM-MM hybrid algorithm is derived for maximizing the data likelihood and clustering the cells. We perform a series of simulation studies to evaluate the proposed method and demonstrate the outstanding performance of EDClust. Comprehensive benchmarking on four real scRNA-seq datasets with various tissue types and species demonstrates the substantial accuracy improvement of EDClust compared to existing methods. The R package is freely available at https://github.com/weix21/EDClust. Supplementary data are available at Bioinformatics online.
Bibliography:The authors wish it to be known that, in their opinion, the Xin Wei and Ziyi Li should be regarded as Joint First Authors.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btac168