GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles
Open Access
- 7 July 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Communications
- Vol. 12 (1), 1-11
- https://doi.org/10.1038/s41467-021-24489-8
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.Funding Information
- National Natural Science Foundation of China (11671256(Yu))
- Shanghai Jiao Tong University (STAR Grant (20190102))
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