cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Christina Bligaard Pedersen, Søren Helweg Dam, Mike Bogetofte Barnkob, Michael D. Leipold, Noelia Purroy, Laura Z. Rassenti, Thomas J. Kipps, Jennifer Nguyen, James Arthur Lederer, Satyen Harish Gohil, Catherine J. Wu, Lars Rønn Olsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.
Original languageEnglish
Article number1698
JournalNature Communications
Number of pages12
Publication statusPublished - 2022


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