GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data
- 1KISTI
Abstract
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.
Related
- Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type, and sex specificity of gene expression with novel genetic risk for MERTK in female
- Comparative transcriptomic analysis of intestinal Tuft cells reveals species selectivity of DCLK1 expression
- Characterisation of Genetically-Derived Patient Clusters Highlights Genetic Heterogeneity of Inflammatory Bowel Disease
- Integrated transcriptomic and epigenomic profiling of human cartilage reveals coordinated changes in fibronectin expression, splicing, and receptor signaling during osteoarthritis progression
- SVC: A Vision Transformer-based Spatially and Virtually Embedded Cell Model for Deciphering Subcellular Spatial Transcriptomic Heterogeneity
BibTeX
@misc{paik2026golden,
title = {{GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data}},
author = {H. Paik},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.18.759920},
url = {https://www.biorxiv.org/content/10.64898/2026.10.18.759920v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - GOLDEN: Gene expression and allele-informed decomposition for genetic heterogeneity in spatial transcriptomic data
AU - H. Paik
PY - 2026
DA - 2026/10/18
PB - bioRxiv
DO - 10.64898/2026.10.18.759920
UR - https://www.biorxiv.org/content/10.64898/2026.10.18.759920v1
AB - 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.
ER -