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. 2020 Apr 25;21:158. doi: 10.1186/s12859-020-3482-1

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© The Author(s). 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Analysis of mouse brain dataset with more than one million cells. a An overview of the GiniClust3 pipeline. Input single-cell expression matrix is clustered based on features selected by Gini index (GiniIndexClust) and by Fano factor (FanoFactorClust), respectively. The results are then integrated using a cluster-aware, weighted consensus clustering algorithm (ConsensusClust). b UMAP visualization of the gene expression patterns based on Fano-factor (top) and Gini index (bottom) selected features, respectively. Consensus clustering results are indicated by different colors. c The proportion of rare cell cluster in entire population. d Heatmap of cell type mapping of common and rare clusters from scMCA analysis. Bar plot in the top indicates the cell number for each cluster