PRISM: Phenotype-Resolved Inference in Single-Cell Mixed Models via Latent Disease States and Contextualized Differential Expression

Standard single-cell differential expression (DE) analysis identifies genes that change across conditions, but it usually overlooks two key sources of heterogeneity that single-cell data are uniquely positioned to reveal: which cells within a donor are truly disease-affected, and how disease effects depend on cell state or subject-level context. Assigning every cell the diagnosis of its donor can contaminate DE signal when disease penetrance is partial, while modeling each gene with a single disease effect cannot capture heterogeneous responses across cell populations. We present PRISM (Phenotype-Resolved Inference in Single-cell Mixed models), a negative-binomial mixed-effects framework that augments standard DE analysis with three outputs: a context-DE vector $\theta_g$ that tests whether disease effects vary along a biological axis $z_{ij}$ (such as cell state, sex, or age); a cell-level disease posterior $q_{ij}$ that provides unsupervised disease annotation; and a subject-level disease burden $\rho_i$. Naive likelihood-only inference of $q_{ij}$ is not identified onto the disease axis when nuisance variation (batch, cell-cycle, dominant subtypes) competes with disease; we resolve this with a closed-form 1-D Wasserstein-2 projection of the disease-arm posterior onto a bimodal reference marginal that enforces the \{healthy, affected\} structure implied by the disease label.