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

Article · Miller BF, Huang F, Atta L, Sahoo A, Fan J

- Archive id: doi:10.1038/s41467-022-30033-z
- Published: 2022-04-29
- Venue: Nature communications
- DOI: https://doi.org/10.1038/s41467-022-30033-z
- PMID: 35487922
- PMCID: PMC9055051
- Citations: 209
- Source URL: https://doi.org/10.1038/s41467-022-30033-z
- PDF: https://europepmc.org/articles/PMC9055051/pdf
- Provenance: europepmc

## 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 .

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [Single-Cell & Spatial Omics](https://space-kitty.com/topics/single-cell-spatial)

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