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Rctd inputs a spatial transcriptomics dataset, which consists of a set of pixels, which are spatial locations that measure rna counts across many genes. The authors demonstrate the strengths of rctd, cell2location, and spatialdwls for their performance, while also revealing the limitations of many methods when compared to simpler baselines. Here, we introduce rctd, a supervised learning approach to decompose rna sequencing mixtures into single cell types, enabling the assignment of cell types to spatial transcriptomic pixels.
To run rctd, we first install the spacexr package from github which implements rctd. Here, we will explain how the analysis occured for our paper ‘robust decomposition of cell type mixtures in spatial transcriptomics’, which introduces and validates the rctd r package. Robust cell type decomposition (rctd) is a statistical method for decomposing cell type mixtures in spatial transcriptomics data
In this vignette, we will use a simulated dataset to demonstrate how you can run rctd on spatial transcriptomics data and visualize your results.
Here we show how to perform cell type deconvolution using rctd (robust cell type decomposition) The first step is to read in the reference dataset and create a reference object
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