Semi-supervised Deconvolution of Spatial Transcriptomics in Breast Tumors

Spatial transcriptomic profiling allows studying the heterogeneity of cell types and their spatial distribution in the context of the tissue microenvironment. However, current high-throughput spatial transcriptomic technologies are low resolution, i.e. measurement from each capture location involves a mixture of multiple cells. This problem hinders downstream analysis of intercellular interactions especially in complex tissues such as tumors. We propose two approaches for decomposing spatial transcriptomics without the need for paired single-cell RNA-seq data: non-negative matrix factorization for unsupervised discovery of major cell types, and a semi-supervised autoencoder model for further separation of cell states with incorporation of known marker gene-sets as prior knowledge regularization. We present preliminary insights into tumor-immune interactions in breast cancer tumors and benchmark performance on spatial data simulated from single-cell peripheral blood cells.