Journal
BIOINFORMATICS
Volume 37, Issue 15, Pages 2212-2214Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa956
Keywords
-
Categories
Funding
- NIH [1U19MH114830, 1U19MH114821]
- Howard Hughes Medical Institute
Ask authors/readers for more resources
The tools presented in this study offer a method to evaluate cluster stability through subsampling of cells, which can guide parameter selection and aid in biological interpretation.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available