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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. 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. 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.
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To run rctd, we first install the spacexr package from github which implements rctd. 文中展示了 RCTD 在模拟数据集上检测混合物和识别细胞类型的能力。 此外，RCTD 在小鼠大脑的 Slide-seq 和 Visium 数据集中准确地再现了已知的细胞类型和亚型定位模式。 RCTD (Robust Cell Type Decomposition)，是一种用于将单细胞RNA测序数据中的 细胞类型注释转移到空间转录组学数据上的方法。
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. On this page, you can find the links and phone numbers in the meeting agendas, as well as the board's two week calendar of scheduled events (see the recent notices section below) To watch the meetings live, visit rtd’s board meetings youtube channel.
