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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. Dentified transcript programs from spatial data in cell type context 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.
Robust cell type decomposition (rctd) is a statistical method for decomposing cell type mixtures in spatial transcriptomics data Robust cell type decomposition (rctd) inputs a spatial transcriptomics dataset, which consists of a set of pixels, which are spatial locations that measure rna counts across many genes In this vignette, we will use a simulated dataset to demonstrate how you can run rctd on spatial transcriptomics data and visualize your results.
To run rctd, we first install the spacexr package from github which implements rctd.
In this document, we run spacexr’s rctd algorithm on simple synthetic data to infer that the weights matrix should be interpreted as the proportion of rna molecules originating from each cell type in each spot, rather than the fraction of cells assigned to each cell type.
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