GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

View/ Open
Published Version
https://doi.org/10.1186/s13059-016-1010-4Metadata
Show full item recordCitation
Jiang, Lan, Huidong Chen, Luca Pinello, and Guo-Cheng Yuan. 2016. “GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.” Genome Biology 17 (1): 144. doi:10.1186/s13059-016-1010-4. http://dx.doi.org/10.1186/s13059-016-1010-4.Abstract
High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1010-4) contains supplementary material, which is available to authorized users.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930624/pdf/Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:27822376
Collections
- HMS Scholarly Articles [17875]
- SPH Scholarly Articles [6353]
Contact administrator regarding this item (to report mistakes or request changes)