Welcome to RCTD, an R package for learning cell types and cell type-specific differential expression in spatial transcriptomics data. RCTD inputs a spatial transcriptomics dataset, …

To enable cross-platform learning in RCTD, we have developed and validated a platform effect normalization procedure. We demonstrate that RCTD can accurately discover localization of …

Mar 28, 2025 · 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 …

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

With the spatial “map” and single-cell “dictionary” available, the deconvolution can begin using Robust Cell Type Decomposition (RCTD). The goal of RCTD is to use the pure cell-type …

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 …

Feb 18, 2021 · 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 …

RCTD learns cell type profiles from the scRNA-seq dataset, and uses these to label the spatial transcriptomics pixels as cell types. RCTD has been tested across a variety of spatial …

Feb 26, 2024 · 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 …

May 8, 2020 · Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles …