It includes private and public schools, public districts and other public units (i.e., regional programs, dept Of corrections, special ed cooperatives and vocational schools). 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 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 To run rctd, we first install the spacexr package from github which implements rctd. We demonstrate rctd’s ability to detect mixtures and identify cell types on simulated datasets
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