Generative cell phenotyping with structured latent populations

Abstract

Flow cytometry produces high-dimensional single-cell protein measurements central to immunophenotyp ing and clinical monitoring. Yet analysis still relies largely on manual gating, which is labour-intensive, poorly reproducible, and ill-suited to large marker panels. Existing computational approaches address classification or discovery in isolation, treating cell-type identity as a post-hoc annotation rather than as part of the generative model itself. We present MARVIN, a semi-supervised variational autoencoder that encodes the assumption that cells organise into discrete populations with continuous intra-population variability through a Gaussian mixture prior in the latent space. Because each component represents a distinct cell population, classification, discovery, and density estimation emerge as complementary views of the same representation. On public benchmarks, MARVIN matches or exceeds existing methods using as few as 10% labelled cells. Trained exclusively on healthy samples, it identifies leukaemic cells through elevated reconstruction error, providing an unsupervised anomaly detection signal. On paired stimulation data, it maintains stable population assignments while capturing condition-specific shifts in abundance and marker expression at patient-level resolution. MARVIN is open-source and designed for local deployment, adapting to institution-specific panels and instruments.