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.
Package details |
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Bioconductor views | CellBasedAssays Clustering RNASeq Sequencing SingleCell Software Visualization |
Maintainer | |
License | GPL-3 + file LICENSE |
Version | 1.1.0 |
URL | https://github.com/jichunxie/CDI |
Package repository | View on GitHub |
Installation |
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