{"id":"doi:10.1038/s41467-022-30033-z","kind":"article","title":"Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data","authors":["Miller BF","Huang F","Atta L","Sahoo A","Fan J"],"author_count":5,"year":2022,"published":"2022-04-29","venue":"Nature communications","cited_by":209,"doi":"10.1038/s41467-022-30033-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-022-30033-z","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-022-30033-z","updated":null,"pmid":"35487922","pmcid":"PMC9055051","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9055051/pdf","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_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data\" by Miller BF, Huang F, Atta L et al., https://doi.org/10.1038/s41467-022-30033-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"single-cell-spatial","name":"Single-Cell & Spatial Omics","domain_slug":"biotech"}],"cited_in_dossiers":[]}