B-T.54: spDDB: Comprehensive benchmarking of spatial deconvolution and domain detection methods for spatial transcriptomics datasets across technologies and tissue types
Authors
Ajita Shree
IIT Kanpur
Aditya V
IIT Kanpur
Tanush Kumar
IIT Kanpur
Hamim Zafar
IIT Kanpur
Keywords
Spatial Transcriptomics, Deconvolution, Domain Detection, Benchmarking
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Spatial transcriptomics has enabled gene expression profiling in a spatial context, but accurately mapping cell types in situ remains challenging. Although numerous deconvolution methods have been developed, comprehensive benchmarking across diverse tissue architectures and technologies is still lacking. Here, we present spDDB (https://github.com/Zafar-Lab/spDDB), benchmarking study for 21 state-of-the-art spatial deconvolution methods, including 7 newly developed methods, evaluated across an exhaustive repository of 37 datasets. Existing studies often rely on oversimplified simulation strategies, necessitated by the absence of ground-truth cell-type proportions. To address this limitation, we developed SynthST, which leverages a deep graph attention network to generate synthetic cell-type proportions matrices that preserve spatial dependencies learned from real spatial transcriptomic data. These simulated proportions are then used to construct paired spatial gene-expression datasets by sampling expression profiles from matched single-cell data and modeling UMI count distributions using Gaussian process framework.
To enable robust evaluation, we further compiled a curated dataset repository spanning brain, cancer, and organ tissues across multiple technologies, for which realistic simulated datasets were generated using SynthST. In addition, spDDB introduces a comprehensive evaluation framework comprising five spatial bivariate metrics (including a novel bivariate Geary's C), three rare cell-type metrics, and five cell-shape characterization metrics, enabling more holistic assessment of deconvolution methods. Further, we introduce a benchmarking pipeline evaluating 18 spatial domain detection methods across 36 datasets (21 real and 15 simulated) spanning six technologies. Finally, we provide practical guidelines and recommendations to assist researchers in selecting optimal methods across diverse experimental settings.
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