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

• Published in: Nature communications Vol. 13; no. 1; pp. 1698 - 12
• Main Authors: Pedersen, Christina Bligaard, Dam, Søren Helweg, Barnkob, Mike Bogetofte, Leipold, Michael D., Purroy, Noelia, Rassenti, Laura Z., Kipps, Thomas J., Nguyen, Jennifer, Lederer, James Arthur, Gohil, Satyen Harish, Wu, Catherine J., Olsen, Lars Rønn
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.03.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2401; 631/114/2784; 631/114/794; 82/1; 82/80; Data analysis; Datasets; Flexibility; Flow cytometry; Flow Cytometry - methods; Humanities and Social Sciences; Integration; Massive data points; multidisciplinary; Robustness; Science; Science (multidisciplinary); Statistics; Technology; Variance
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29383-5

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. Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. Here, the authors present a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques.