Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

Brendan F. Miller, Feiyang Huang, Lyla Atta, Arpan Sahoo and Jean Fan ()
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Brendan F. Miller: Johns Hopkins University
Feiyang Huang: Johns Hopkins University
Lyla Atta: Johns Hopkins University
Arpan Sahoo: Johns Hopkins University
Jean Fan: Johns Hopkins University

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

Abstract: Abstract Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .

Date: 2022
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DOI: 10.1038/s41467-022-30033-z

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