To address these challenges, we presentSpatial transcriptomics deconvolution usingMARker-gene-assistedTopic model (SMART), a marker-gene-assisted deconvolution method based on semi-supervised topic models (Fig.1). In natural language processing, the topic models were used to identify the topic distribut...
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-c...
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, condi
Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types ...
In this study, we present the Spatial Transcriptomics deconvolution using Graph Convolutional Networks (STdGCN) as a novel approach for ST data cell-type deconvolution. Our model leverages graph convolutional networks (GCN), a widely used graph-based deep learning model. Notably, STdGCN integrates ...
Deconvolution of Spatial Transcriptomics Data Deconvolution aims to infer cell type composition and spatial distribution within each ST spot. Given that certain ST technologies lack single-cell resolution, AI methods are essential for accurate deconvolution. ...
Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis...
High-throughput spatial transcriptomics data pose new computational challenges and opportunities that require novel methods and tools. Some of the key problems include data preprocessing, cell-type deconvolution, identification of spatially variable genes, and inference of cell–cell interactions (Fig.3)....
deconvolution of cell types [71]. STRIDE is a deep learning-based method that integrates spatial transcriptomics and scRNA-seq data for cell type identification and deconvolution of spatial distribution [72]. Previous studies have conducted comprehensive benchmarking of common deconvolution methods, ...
deconvolution of cell types [71]. STRIDE is a deep learning-based method that integrates spatial transcriptomics and scRNA-seq data for cell type identification and deconvolution of spatial distribution [72]. Previous studies have conducted comprehensive benchmarking of common deconvolution methods, ...