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bioRxiv · 10.1101/246439

A cluster-aware, weighted ensemble clustering method for cell-type detection

Tsoucas, D.·Yuan, G.-C.
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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, called 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 very large datasets.

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Tsoucas, D., Yuan, G.-C.. 2018-01-10. A cluster-aware, weighted ensemble clustering method for cell-type detection. https://doi.org/10.1101/246439

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