GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.
GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.
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DOI：
10.1186/s13059-016-1010-4
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发表时间：
2016-07-01
期刊：
影响因子：
12.3
通讯作者：
Yuan GC
中科院分区：
文献类型：
--
作者：
Jiang L;Chen H;Pinello L;Yuan GC
高通量单细胞技术具有发现新细胞类型的巨大潜力;然而，检测与大群体不同的罕见细胞类型仍然具有挑战性。我们提出了一种新的计算方法，称为GiniClust，以克服这一挑战。对基准数据集的验证表明，GiniClust实现了高灵敏度和特异性。将GiniClust应用于公共单细胞RNA-seq数据集，发现了以前未被识别的罕见细胞类型，包括小鼠胚胎干细胞中的Zscan4表达细胞和小鼠皮质和海马中的血红蛋白表达细胞。GiniClust还能正确检测癌细胞群中混合的少量正常细胞。本文的在线版本（doi：10.1186/s13059 - 016 - 1010 - 4）包含补充材料，可供授权用户使用。
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. The online version of this article (doi:10.1186/s13059-016-1010-4) contains supplementary material, which is available to authorized users.