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

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…

• NS
  National Science Foundation

  Award: EF0849615
• UO
  University of North Carolina at Charlotte