CytoExploreR is an open-source software package unmatched in its capacity to facilitate end-to-end analysis of cytometry data, including data import, annotation, visualisation, spectral deconvolution (compensation and unmixing), transformation, normalisation, integration, gating (manual and automated), clustering, dimensionality reduction and computation of statistics. In this presentation I will explore the plethora of new computational tools available within CytoExploreR to facilitate efficient, scalable, and reproducible analysis of large high-dimensional datasets. I will describe the new suite of tools available to visualise spectral profiles and perform spectral unmixing on spectral flow cytometry data. Users of conventional flow cytometry will be introduced to a new hybrid algorithm that combines conventional automated compensation and Autospill to automate the computation of accurate spillover matrices. Self-Organising Maps (SOMs) will be proposed as a mechanism for data compression to facilitate the analysis of enormous datasets without the need for additional computational resources. In addition to data compression, I will demonstrate how SOMs can be used to identify subtle phenotypic differences between samples or experimental groups, important phenotypes that may be lost when performing gating or clustering. I will also introduce a new framework that provides a series of metrics for comparing and clustering samples based on compositional or distributional similarity. CytoExploreRUI will be presented as an accessory package that provides pre-built interactive modules around CytoExploreR functions to facilitate the development of custom shiny applications tailor-made for routine analysis pipelines. Finally, I will outline plans for the long-term support, continued development and integration of CytoExploreR with commercially available software platforms.