Trust-VC: Multi-View Clustering for Transcriptomics via Trust-Aware Virtual-Prior Residual Fusion

Multi-view clustering (MVC) of paired multi-omics data must reconcile complementary but unequally reliable views, and the problem becomes harder when external representations from pretrained foundation models are introduced. We present Trust-VC, a reliability-aware MVC framework that treats a foundation-model embedding as a virtual-cell prior rather than an equally trusted observed view. RNA is directly measured in all evaluated datasets. We use a fixed RNA-derived representation as the reference for fusion, and express contributions from the measured auxiliary modality and the virtual prior as gated residual corrections relative to that reference. For each cell, virtual-prior trust combines cross-modal consistency, a learned latent-dispersion proxy, and a self-supervised assignment confidence gain proxy. A reference-preservation objective penalizes excessive net displacement from the fixed RNA reference, while partial-to-complete distillation aligns the two residual-enhanced partial views with the complete view without updating the RNA reference. Extensive experiments on eight paired single-cell multi-omics datasets demonstrate that Trust-VC not only achieves the best performance in 28 of 32 dataset–metric comparisons, with up to a 10.8 percentage-point ACC improvement, but also regulates transferred corrections in fixed-checkpoint prior-permutation diagnostics.