Abstract
<title>Abstract</title> <p>Functional remodeling of tissues is driven not only by changes in cellular composition, but also by shifts in how individual cell types engage biological programs across disease states, biological transitions and therapeutic contexts. Single-cell transcriptomics provides a direct view of cell-type functional heterogeneity, but its limited cohort scale restricts population-level association analyses, and conventional single-cell profiling lacks the spatial context needed to localize functional changes within tissues. Conversely, bulk RNA-seq cohorts and spatial transcriptomic profiles offer large-scale or spatially resolved measurements, yet their mixed expression signals obscure the cell-type origins of functional program activity. Here, we present FuncDECODE, a computational framework that infers the relative contributions of different cell types to functional programs from mixed transcriptomic profiles. By learning from single-cell references, FuncDECODE converts bulk samples and spatial spots into interpretable cell type–functional program features, enabling functional remodeling to be assigned to its likely cellular sources. Across bulk and spatial benchmarks, FuncDECODE consistently recovered cell-type-resolved functional contribution patterns and remained robust across donors, sequencing technologies, biological states and alternative functional program definitions. Across bulk and spatial transcriptomic applications, FuncDECODE revealed biologically and clinically relevant cell type–program remodeling. These features resolved immune-state transitions after vaccination, identified prognostic functional programs in breast cancer, and localized treatment-associated microenvironmental remodeling within spatial tissue architecture. Together, FuncDECODE provides a general framework for connecting single-cell functional heterogeneity with cohort-scale disease variation and spatial tissue organization.</p>