GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection

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...

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Bibliographic Details
Published in:Genome Biology 2018-05, Vol.19 (1), p.58-58, Article 58
Main Authors: Tsoucas, Daphne, Yuan, Guo-Cheng
Format: Article
Language:English
Subjects:
Clustering
Computer applications
Consensus clustering
Data mining
Datasets
Ensemble clustering
Gene expression
Genomes
Gini index
International conferences
Method
Methods
scRNA-seq
Single-cell
Stem cells
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Summary: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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-018-1431-3