期刊
GENOME BIOLOGY
卷 19, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-018-1431-3
关键词
Clustering; Consensus clustering; Ensemble clustering; Single-cell; scRNA-seq; Gini index; Rare cell type
资金
- Claudia Barr Award
- Bridge Award
- NIH grant [R01HL119099]
- NIH training grant [T32GM074897]
Single-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here, we present a new computational method, GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to large datasets.
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