title: Identification of cell types from single-cell transcriptomes using a novel clustering method.
abstract: BACKGROUND
  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.
  
  
  METHODS
  The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq.
authors: Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
authors: Chen
authors: C
authors: Xu
authors: Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
authors: Zhengchang
authors: Z
authors: Su
mesh_terms: D000465:Algorithms; D000818:Animals; D019070:Cell Lineage / Q000235:genetics*; D016000:Cluster Analysis; D004622:Embryo, Mammalian / Q000166:cytology / Q000378:metabolism*; D005786:Gene Expression Regulation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D051379:Mice; D009369:Neoplasms / Q000235:genetics*; D011381:Programming Languages; D059010:Single-Cell Analysis / Q000379:methods*; D059467:Transcriptome