GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
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CitationJiang, 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.
AbstractHigh-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.
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