SuperCellCyto: enabling efficient analysis of large scale cytometry datasets
- Author(s)
- Putri, GH; Howitt, G; Marsh-Wakefield, F; Ashhurst, TM; Phipson, B;
- Details
- Publication Year 2024-04-08,Volume 25,Issue #1,Page 89
- Journal Title
- Genome Biology
- Abstract
- Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).
- Publisher
- BMC
- Keywords
- Batch correction; Bioinformatics; CITEseq; Clustering; Computational analysis; Cytof; Cytometry; Data compression; Dimensionality reduction; Supercell
- Research Division(s)
- Bioinformatics
- PubMed ID
- 38589921
- Publisher's Version
- https://doi.org/10.1186/s13059-024-03229-3
- Open Access at Publisher's Site
https://doi.org/10.1186/s13059-024-03229-3- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-04-18 09:15:20
Last Modified: 2024-04-18 09:24:22