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Clustering Deviation Index (CDI)

Bioconductor version: Release (3.18)

Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells.

Author: Jiyuan Fang [cre, aut] , Jichun Xie [ctb], Cliburn Chan [ctb], Kouros Owzar [ctb], Liuyang Wang [ctb], Diyuan Qin [ctb], Qi-Jing Li [ctb], Jichun Xie [ctb]

Maintainer: Jiyuan Fang <jfanglovestats at>

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biocViews CellBasedAssays, Clustering, RNASeq, Sequencing, SingleCell, Software, Visualization
Version 1.0.2
In Bioconductor since BioC 3.18 (R-4.3) (< 6 months)
License GPL-3 + file LICENSE
Depends R (>= 3.6)
Imports matrixStats, Seurat, SeuratObject, stats, BiocParallel, ggplot2, reshape2, grDevices, ggsci, SingleCellExperiment, SummarizedExperiment, methods
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