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

Author:
Tsoucas, Daphne, Yuan, Guo-Cheng
Source:
Genome biology 2018 v.19 no.1 pp. 58
ISSN:
1474-760X
Subject:
animal tissues, computer software, data collection, genome, genomics, molecular genetics
Abstract:
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.
Agid:
5946793