scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq

Single-cell RNA-seq studies of viral infection are limited by sparse viral reads, under-labeled infected cells, and bystander responses that confound differential expression (DE) analysis. We introduce scDEcrypter, a penalized two-way mixture model that leverages partial infection and cell type labels to estimate latent cell-state probabilities. scDEcrypter provides fast permutation-based approximate p-values for prioritizing infection-associated genes while accounting for the uncertainty in cell-state. In simulations and applications to two viral infection datasets, scDEcrypter improved recovery of infected cell states and identified more biologically coherent infection-associated genes and enriched pathways.