Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), tha...
To estimate cell-type proportions across SRT spots using gene expression, spatial location, and histology information, we developed SpaDecon, a semi-supervised learning-based method for cell-type deconvolution that can be applied to spatial barcoding-based SRT data. We analyzed eight SRT datasets, i...
However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial ...
However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe...
STdGCN is designed for cell-type deconvolution in ST data by using scRNA-seq data as a reference (Fig.1). The underlying hypothesis is that both ST data and scRNA-seq data share common cell types, and their cell type gene transcript signatures exhibit similarities. In short, the normalized...
Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data Nat Commun, 13 (2022) Google Scholar 75 Q. Song, J. Su DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence Brief Bioinform, 22 (2021) Google Scholar...
Cell-type deconvolution using cell2location 使用cell2location进行细胞类型解卷积 Para_01 我们使用了 cell2location (v.0.1.3) 来估计 Visium 位置的细胞类型比例,以前述的乳腺癌图谱作为参考。 为了完成此任务,我们在包含 313 个基因的 Xenium 重复样本子集上进行操作,并将 Visium 数据集和乳腺癌图谱限制在这组...
Cell-type deconvolution using cell2location 使用cell2location进行细胞类型解卷积 Para_01 我们使用了 cell2location (v.0.1.3) 来估计 Visium 位置的细胞类型比例,以前述的乳腺癌图谱作为参考。 为了完成此任务,我们在包含 313 个基因的 Xenium 重复样本子集上进行操作,并将 Visium 数据集和乳腺癌图谱限制在这...
前面介绍过整合scRNA-seq和空间转录组数据主要有(1)映射(Mapping)和(2)去卷积(Deconvolution)两种方法。前面空转 | 结合scRNA完成空转spot注释(Seurat Mapping) & 彩蛋(封面的空转主图代码)介绍了使用Seurat Mapping的方式进行spot注释,本文介绍一种经典的解卷积方法-SPOTlight。 生信补给站 2023/08/25 2.2K0 绝大...
For spatial transcriptome data, deconvolution is required to determine the cell type composition of each spot based on the generated expression matrix. Therefore, the general data processing pipeline of spatial barcoding-based transcriptomics can be split into (1) upstream preprocessing to generate the ...