High-dimensional flow cytometry is routinely adopted as a high-throughput technology to perform deep phenotypic profiling of cellular systems in preclinical and clinical settings. However, several factors can introduce technical variance or batch effects into these datasets that impede our ability to compare datasets. Samples may be collected from multiple patients across many timepoints, undergo different processing (e.g. fixed, frozen, stimulated), staining with different antibody panels, and be run on separate instruments. CytoWarp will be presented as a new computational tool to allow the removal of batch effects in cytometry data using an interactive normalisation pipeline which provides users with fine control over data segmentation, reference assignment and alignment. During this presentation, we will outline the key components of the CytoWarp algorithm that address limitations of existing normalisation techniques, and provide examples of how it can be used to normalise multi-centre longitudinal datasets. CytoWarp will also be used as an example of how CytoExploreRUI can be used to create custom interactive applications that leverage CytoExploreR for routine analysis pipelines.