Robust 3D reconstruction and cell-type deconvolution approach for spatial transcriptomics by hybrid graph learning and iterative computation

Spatial transcriptomics data provides new methods of exploring gene expressions. However, the results usually do not contain cell-level proportions, leaving a gap between the new technologies and the corresponding analysis. To address the issues, we propose a novel framework, OptiGraph3D, in this paper. Our approach starts with a pivoted sample-level sequential registration, eliminating potential cumulative errors in multiple registrations. The registration results provide sufficient spatial prior for our cell-type deconvolving process, which leverages the power of ADMM method by modelling the cell-type deconvolution task as a matrix inverse problem and proposes efficient solvers with guaranteed convergence. Furthermore, the cell-type deconvolution results can in turn perform as supervision for cluster recognition in 3D reconstruction. Our model introduces a semi-supervision training manner by introducing a specific class token in the multi-headed graph attention network, bridging the gap between gene-level expressions of spatial transcriptomics and presenting new angle for sample-wise analysis. Extensive experiments are performed on the human dorsolateral prefrontal cortex (DLPFC) and the developing human embryonic heart dataset, suggesting our approach's superiority over other methods.