Over the past decade, advancements in full spectrum flow cytometry and isotope labeling techniques have significantly increased the number of quantifiable markers. Combined with higher cellular throughput, these improvements have generated complex, high-dimensional data that requires advanced computational methods to replace traditional manual gating. The analysis of such data from flow or mass cytometry can be divided into three main steps. First, data pre-processing involves removing dead cells, compensation, and verifying marker expression patterns, typically using commercial software like FlowJo or R packages. Second, within an R workflow, quality control checks and data transformation are performed, followed by FlowSOM clustering on the entire cell dataset using all markers to identify populations; this includes an initial coarse clustering followed by subclustering to detect specific cell types or states. Finally, differential analysis using R statistical workflows compares the abundances of particular subpopulations between different conditions.