GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data

Motivation Spatial transcriptomics (ST) enables exploration of cellular interactions, including immune responses. However, current methods rely on pathologists, with limitations in addressing genetic heterogeneity, where niches harboring nonclonal cells, including placental or allograft tissues, are present. Deciphering these interindividual mixtures remains challenging. Results For deciphering interindividual heterogeneity in ST, we developed GOLDEN (Gene expressiOn and aLlele-informed DEcomposition for geNetic heterogeneity), which involves an allele-aware multitask network that encodes spot-level expressions into shared latent representations. Incorporating allele features, dual heads classify spot identity (target, nontarget, or mixed) and estimate target fractions. We utilized synthetic ST data with spot-level ground truth using semiallogeneic single-cell profiles. For these synthetic benchmarks, GOLDEN achieved reliable deconvolution, with high correlation (Pearson's r = 0.977) and low error (mean absolute error (MAE) = 0.045) in fraction estimation and consistent predictions across real-world STs. These predictions were genetically validated. Using GOLDEN, resolving spot-level genetic heterogeneity is feasible. Availability and implementation GOLDEN and the simulation code for ST are available at https://github.com/othertics/GOLDEN.