Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Abstract

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

Abstract

The deconvolution of cell types is challenging in spatially-resolved transcriptomics. Here, the authors present SpatialDecon, a method for the deconvolution and quantification of cell types in spatial transcriptomics data, and show how it can be used to analyse immune response heterogeneity in cancer.