Identification of cell types from single-cell transcriptomes using a novel clustering method

• Published in: Bioinformatics (Oxford, England) Vol. 31; no. 12; pp. 1974 - 1980
• Main Authors: Xu, Chen, Su, Zhengchang
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.06.2015
• Subjects: Algorithms; Animals; Cell Lineage - genetics; Cluster Analysis; Embryo, Mammalian - cytology; Embryo, Mammalian - metabolism; Gene Expression Regulation; High-Throughput Nucleotide Sequencing - methods; Humans; Mice; Neoplasms - genetics; Original Papers; Programming Languages; Single-Cell Analysis - methods; Transcriptome
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btv088

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. Availability and implementation: The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq. Contact:  zcsu@uncc.edu Supplementary information:  Supplementary data are available at Bioinformatics online.