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Updated: Jul 11, 2025
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
A new and effective two-step clustering approach for single cell RNA sequencing data
Ruiyi Li1,2, Jihong Guan3, Zhiye Wang2
1Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, and School of Medicine, Tongji University, 1239 Siping Road, 200092, Shanghai, China.
Background:
The rapid devolvement of single cell RNA sequencing (scRNA-seq) technology leads to huge amounts of scRNA-seq data, which greatly advance the research of many biomedical fields involving tissue heterogeneity, pathogenesis of disease and drug resistance etc. One major task in scRNA-seq data analysis is to cluster cells in terms of their expression characteristics. Up to now, a number of methods have been proposed to infer cell clusters, yet there is still much space to improve their performance.
Results:
In this paper, we develop a new two-step clustering approach to effectively cluster scRNA-seq data, which is called TSC - the abbreviation of Two-Step Clustering. Particularly, by dividing all cells into two types: core cells (those possibly lying around the centers of clusters) and non-core cells (those locating in the boundary areas of clusters), we first clusters the core cells by hierarchical clustering (the first step) and then assigns the non-core cells to the corresponding nearest clusters (the second step). Extensive experiments on 12 real scRNA-seq datasets show that TSC outperforms the state of the art methods.
Conclusion:
TSC is an effective clustering method due to its two-steps clustering strategy, and it is a useful tool for scRNA-seq data analysis.
