Name
Title | ||
|---|---|---|
Identification of cell types from single-cell transcriptomes using a novel clustering method. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1093/bioinformatics/btv088 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Transcriptome,Cell type,Cell,Cluster analysis | Journal | 31 |
Issue | ISSN | Citations |
12 | 1367-4803 | 34 |
PageRank | References | Authors |
2.07 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Xu | 1 | 57 | 15.14 |
| Zhengchang Su | 2 | 34 | 2.07 |