Novel tensor decomposition-based approach for cell type deconvolution in Visium datasets when reference scRNA-seqs include multiple minor cell types

Abstract

We have applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to integrate multiple Visium datasets, as a platform for spatial gene expression profiling (spatial transcriptomics). As a result, TD-based unsupervised FE successfully obtains singular value vectors consistent with the spatial distribution, that is, singular value vectors with similar values are assigned to neighboring spots. Furthermore, TD-based unsupervised FE successfully infers the cell-type fractions within individual Visium spots (i.e., successful deconvolution) by referencing single-cell RNA-seq experiments that include multiple minor cell types, for which other conventional methods--RCTD, SPOTlight, SpaCET, and cell2location--fail. Therefore, TD-based unsupervised FE can be used to perform deconvolution even when other conventional methods fail because it includes multiple minor cell types in the reference profiles, although it cannot be used in typical cases. TD-based unsupervised FE is thus expected to be applied to a wide range of deconvolution applications.