Protein expression-based single-cell cytometry has evolved immensely over the past decades. 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. c圜ombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. We demonstrate that c圜ombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets.

Here, we present c圜ombine, 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. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches.

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses.