CDI
Clustering Deviation Index (CDI)
Bioconductor version: 3.23 · Package version: 1.10.0
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CDI") Details
| Maintainer | Jiyuan Fang <jfanglovestats@gmail.com> |
| Author | Jiyuan Fang [cre, aut] (ORCID: <https://orcid.org/0000-0002-5004-4138>), Jichun Xie [ctb], Cliburn Chan [ctb], Kouros Owzar [ctb], Liuyang Wang [ctb], Diyuan Qin [ctb], Qi-Jing Li [ctb] |
| License | GPL-3 + file LICENSE |
| URL | https://github.com/jichunxie/CDI |
| Bug Reports | https://github.com/jichunxie/CDI/issues |
| Downloads rank | 217 |
| Source branch | RELEASE_3_23 |
| biocViews | CellBasedAssays, Clustering, RNASeq, Sequencing, SingleCell, Software, Visualization |
Documentation
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Dependencies
Depends: R (>= 3.6)
Imports: matrixStats, SeuratObject, Seurat, stats, BiocParallel, ggplot2, reshape2, grDevices, ggsci, SingleCellExperiment, SummarizedExperiment, methods
Suggests: knitr, rmarkdown, RUnit, BiocGenerics, magick, BiocStyle