ArticlebioRxiv : the preprint server for biology2026
scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
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 labels for infection status and additional variables such as cell type. Our approach employs data-splitting to avoid double-dipping and enables fast, likelihood-based inference for DE analysis. Through simulations and applications on two different viral infection datasets, scDEcrypter demonstrated improved recovery of infected cell states and identified more biologically coherent infection-associated genes and enriched pathways.
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Registered trials
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