Welcome to RCTD, an R package for learning cell types and cell type-specific differential expression in spatial transcriptomics data. To enable cross-platform learning in RCTD, we have developed and validated a platform effect normalization procedure. 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.
We demonstrate that RCTD can accurately discover localization of cell. Robust Cell Type Decomposition (RCTD) is a statistical method for decomposing cell type mixtures in spatial transcriptomics data. 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.
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. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for. In order to run Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a single cell RNA-seq reference (as a Seurat RDS file) must be inputted along with a spatial transcriptomics dataset.
Here we show how to perform cell type deconvolution using RCTD (Robust Cell Type Decomposition). RCTD inputs a spatial transcriptomics dataset, which consists of a set of.
- Interpreting RCTD weights - GitHub Pages.
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