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.
Highlights
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
A solution is offered by gene expression deconvolution, a class of algorithms designed to quantify cell populations using gene expression data (Fig. 1)
In a non-small cell lung tumor, we demonstrate the use of our method to map the cell-type composition and spatial organization of a tumor’s immune infiltrate
Summary
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. Single-cell RNA sequencing defines the cell populations present within a tissue This catalog of cell types begs a question that scRNA-seq cannot answer: how are these cell types arranged within tissues? In a non-small cell lung tumor, we demonstrate the use of our method to map the cell-type composition and spatial organization of a tumor’s immune infiltrate. These measurements reveal the spatial organization of cell types defined by scRNA-seq. They give context to genelevel results, resolving whether a gene’s expression pattern reflects differential expression within a cell type or merely differences in cell-type abundance
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