Identification of cell types from single-cell transcriptomes using a novel clustering method. 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. Availability and implementation: The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
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- Park, Seyoung; Zhao, Hongyu: Sparse principal component analysis with missing observations (2019)
- Liu, Hongbing; Li, Weihua; Li, Ran: A comparative analysis of granular computing clustering from the view of set (2017)