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

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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. | Tsoucas and Yuan Genome Biology 2018 19 58 https doi.org 10.1186 s13059-018-1431-3 METHOD Open Access GiniClust2 a cluster-aware weighted ensemble clustering method for cell-type detection Daphne Tsoucas1 2 and Guo-Cheng Yuan1 2 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. Keywords Clustering Consensus clustering Ensemble clustering Single-cell scRNA-seq Gini index Rare cell type Background common cell populations but are not sensitive enough Genome-wide transcriptomic profiling has served as a to detect rare cells. A number of methods have been de- paradigm for the systematic characterization of molecular veloped to specifically detect rare cells 9 12 but the signatures associated with biological functions and features used in these methods are distinct from those disease-related alterations but traditionally this could only distinguishing major populations. Existing methods can- be done using bulk samples that often contain significant not satisfactorily detect both large and rare cell popula- cellular heterogeneity. The recent development of single- tions. A naïve approach combining features that are cell technologies has enabled biologists to dissect cellular either associated with common or rare cell populations heterogeneity within a cell population. Such efforts have fails to characterize either type correctly as a mixed fea- led to an increased understanding of cell-type compos- ture space