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bioRxiv · 10.64898/2026.03.04.709692

SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data

Wang, Q.·Gao, A.·Li, Y.·Khatri, P.·Hu, R.·Huang, J.·Pawitan, Y.·Vu, T. N.·Dinh, H. Q.
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Abstract

Spatial transcriptomics data are largely available with RNA expression alone, limiting the detection of cell states defined by surface protein abundance. The lack of multi-omics spatial data limits the ability to identify immune cells and their signaling in the tumor microenvironment, as most solid tumors are immunologically poor and exhibit protein-RNA abundance discordance in critical immune cell surface markers. Although emerging technologies enable spatial multi-omics profiling, technical and cost constraints remain a hurdle. We introduce SR2P, a stacking-based machine-learning framework for predicting spatial protein abundance from RNA expression. SR2P integrates 11 complementary predictive models and consistently outperforms existing methods across multiple spatial multi-omics benchmark. We showcased an application of SR2P recovered macrophage-enriched regions and identified potential immune markers associated with therapeutic response from head-and-neck squamous cell carcinoma patients. SR2P enables protein-abundance inference from RNA-only spatial data, extending the analytical capabilities of current spatial platforms for studies of tumor immunology.

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Wang, Q., Gao, A., Li, Y., Khatri, P., Hu, R., Huang, J., Pawitan, Y., Vu, T. N., Dinh, H. Q.. 2026-03-07. SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data. https://doi.org/10.64898/2026.03.04.709692

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