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

Patrick Danaher (), Youngmi Kim, Brenn Nelson, Maddy Griswold, Zhi Yang, Erin Piazza and Joseph M. Beechem
Additional contact information
Patrick Danaher: NanoString Technologies
Youngmi Kim: NanoString Technologies
Brenn Nelson: NanoString Technologies
Maddy Griswold: NanoString Technologies
Zhi Yang: NanoString Technologies
Erin Piazza: NanoString Technologies
Joseph M. Beechem: NanoString Technologies

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1038/s41467-022-28020-5

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