Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets
- Journal Title
- Elife
- Publication Type
- Journal epub ahead of print
- Abstract
- Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.
- Publisher
- eLife Sciences
- Research Division(s)
- Bioinformatics; Immunology
- PubMed ID
- 32894218
- Publisher's Version
- https://doi.org/10.7554/eLife.59630
- Open Access at Publisher's Site
https://doi.org/10.7554/eLife.59630- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2020-10-02 10:28:00
Last Modified: 2020-10-02 10:33:33