GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles

Botao Fa, Ting Wei, Yuan Zhou, Luke Johnston, Xin Yuan, Yanran Ma, Yue Zhang and Zhangsheng Yu ()
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Botao Fa: Shanghai Jiao Tong University
Ting Wei: Shanghai Jiao Tong University
Yuan Zhou: Shanghai Jiao Tong University
Luke Johnston: Shanghai Jiao Tong University
Xin Yuan: Shanghai Jiao Tong University
Yanran Ma: Shanghai Jiao Tong University
Yue Zhang: Shanghai Jiao Tong University
Zhangsheng Yu: Shanghai Jiao Tong University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

Date: 2021
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DOI: 10.1038/s41467-021-24489-8

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