scROBM: Role-Guided Bottleneck Model for Single-Cell Latent Representation Learning

Single-cell expressions mix cell identity, condition response, inter-individual background, and technical variation, and reconstruction alone gives no account of which latent coordinates carry which role. We present scROBM , a role-objective-guided bottleneck model for expression-only single-cell representation learning. scROBM assigns three cell-level latent variables to cell identity, perturbation semantics, and donor context, together with a shared donor-level center, and organizes training around two coupled bottlenecks: a label-free count decoder that forces every label-dependent reconstruction signal through the assigned latents, and a conditional-invariance (CI) penalty that suppresses condition information in the joint background means within supported donor–cell-type strata while leaving donor and cell-type differences unconstrained. We evaluate scROBM on four single cell data sets. Donor-associated structure shows the clearest separation, whereas cell-type and condition remain in a high, dataset-dependent range; because every latent is supervised for a single role, the three exports stay interpretable, and the donor-context export resolves donors while exposing a cell-type-dependent condition structure concentrated in the treatment's target compartment.