Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets.
Where this comes from
- Record sourced from PubMed, PMID 32894218.
- Also identified by DOI 10.7554/eLife.59630 and PMC identifier 7500954.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Algorithms
- Computational Biology
- Databases, Factual
- Mass Spectrometry
- Single-Cell Analysis