Journal
BIOINFORMATICS
Volume 31, Issue 12, Pages 1974-1980Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv088
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Funding
- UNC Charlotte Faculty Research Grant [1-11227]
- National Science Foundation [EF0849615, CCF1048261]
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Motivation: The recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes). Results: In this article, we describe a novel algorithm named shared nearest neighbor (SNN)-Cliq that clusters single-cell transcriptomes. SNN-Cliq utilizes the concept of shared nearest neighbor that shows advantages in handling high-dimensional data. When evaluated on a variety of synthetic and real experimental datasets, SNN-Cliq outperformed the state-of-the-art methods tested. More importantly, the clustering results of SNN-Cliq reflect the cell types or origins with high accuracy.
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