We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Evaluating single-cell cluster stability using the Jaccard similarity index.
- Authors
Tang, Ming; Kaymaz, Yasin; Logeman, Brandon L; Eichhorn, Stephen; Liang, Zhengzheng S; Dulac, Catherine; Sackton, Timothy B
- Abstract
Motivation One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor and the resolution parameters, among others. Results Here, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat and estimation of cluster stability using the Jaccard similarity index and providing rich visualizations. Availabilityand implementation R package scclusteval : https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/pyflow%5fseuratv3%5fparameter Tutorial: https://crazyhottommy.github.io/EvaluateSingleCellClustering/.
- Subjects
RNA sequencing; WORKFLOW
- Publication
Bioinformatics, 2021, Vol 37, Issue 15, p2212
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btaa956