AltraFlowSOM: A Semi-Supervised Framework for Imaging Mass Cytometry Phenotyping

Abstract

Imaging Mass Cytometry (IMC) enables the simultaneous quantification of 40+ protein markers at single cell resolution in tissue, however biologically faithful phenotyping at scale remains a critical bottleneck. Unsupervised clustering fragments coherent populations or conversely merges biologically incoherent ones into a single cluster, supervised classifiers impose a closed vocabulary, and the presence of rare subsets (encoding clinically relevant biology) in conjunction with abundant subsets may be detrimental to detection performances. We present AltraFlowSOM, a semi-supervised extension of FlowSOM that embeds partial expert annotations directly into self-organizing map training via a two-layer SuperSOM architecture, balancing label-guided topology anchoring with unsupervised discovery. By anchoring the map to biologically labelled reference points, AltraFlowSOM circumvents the canonical dependency between batch correction and clustering. Evaluated under Leave-one-out cross validation on two independent IMC cohorts, Lupus Nephritis (n=22 ROIs) and Sjogren syndrome (n=10 ROIs), AltraFlowSOM outperformed all unsupervised and supervised baseline on Adjusted Rand Index, F1 scores (macro and weighted), weighted purity and in the identification of rare populations. The median Treg cell recovery exceeded that of all comparator methods. AltraFlowSOM resolves the scalability-alignment-discovery trilemma, by establishing a semi-supervised SOM as a generalizable method for high dimensional IMC phenotyping.