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bioRxiv · 10.1101/2021.06.15.448381

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

Miller, B. F.·Atta, L.·Sahoo, A.·Huang, F.·Fan, J.
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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 developed 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.

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Miller, B. F., Atta, L., Sahoo, A., Huang, F., Fan, J.. 2021-06-16. Reference-free cell-type deconvolution of pixel-resolution spatially resolved transcriptomics data. https://doi.org/10.1101/2021.06.15.448381

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