期刊
GENOME BIOLOGY
卷 17, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-016-1010-4
关键词
Clustering; Single-cell analysis; RNA-seq; qPCR; Gini index; Rare cell type
资金
- Claudia Barr Award
- NIH [R01HL119099]
- NHGRI Career Development Award [K99HG008399]
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.
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