Markus Sommer

Determining whether a cancer patient responds to immunotherapy, detecting contamination in food and water, measuring mitochondrial membrane potential: scientists depend on flow cytometry. Experiments can measure thousands of cells; the bottleneck is analysis, where scientists gate by hand or simply give up on 40-marker spectral panels. The tools that could help are expensive, cloud-based, or need years of programming experience, leaving immunologists with sensitive patient data and limited coding experience stuck. WakaFlockaFlow closes that gap: a free, no-code tool that installs in one command and runs on the lab's own machine. Nothing leaves the building. Point it at raw files and it unmixes spectral signal, names cell populations, scores functional state, pools cohorts onto one map, and compares groups statistically. It exports gating paths to FlowJo as real, editable gates, fitting existing workflows rather than replacing them. Validation runs three ways. On synthetic ground truth it recovers signal, returns zero on a null cohort, and scales properly with effect size. On the Bodenmiller benchmark (real, hand-gated PBMCs) it reproduces the B-cell activation response (pS6 up, p 1.2e-6) and caught a real, documented annotation bug in the data. Live on PyPI today.