Novel Tensor Decomposition-Based Approach for Cell-Type Deconvolution in Visium Datasets with Reference scRNA-Seq Data Containing Multiple Minor Cell Types

Conventional cell-type deconvolution methods, such as Robust Cell-Type Decomposition (RCTD), SPOTlight, Spatial Cellular Estimator for Tumors (SpaCET), and cell2location, often encounter limitations when applied to Visium datasets that include reference profiles with multiple minor cell types. This highlights the necessity for more advanced computational approaches to resolve such challenges. To address this issue, we have employed and refined tensor decomposition (TD)-based unsupervised feature extraction (FE) to integrate multiple Visium datasets, providing a robust platform for spatial gene expression profiling (spatial transcriptomics). Notably, TD-based unsupervised FE successfully retrieves singular value vectors that correspond with spatial distribution; neighboring spots are assigned vectors with comparable values. Additionally, TD-based unsupervised FE demonstrates successful interference of cell-type fractions within individual Visium spots, enabling effective deconvolution even when referencing single-cell RNA-seq datasets containing several minor cell types—a scenario where conventional methods, such as RCTD, SPOTlight, SpaCET, and cell2location, typically prove ineffective. The findings of this study suggest that TD-based unsupervised FE has broad applicability for diverse deconvolution tasks.