In recent years, there has been a rapid development in spatial transcriptomics data analysis techniques. From data preprocessing to the identification of spatially variable genes, clustering analysis, and downstream functional analysis, these steps can now be accomplished through a plethora of computational...
(https://satijalab.org/seurat/) to define cell types. Seurat has gained widespread popularity in the field of spatial transcriptomics due to its robustness, scalability, and user-friendly interface. It offers various modules that facilitate the analysis workflow, enabling researchers to perform tasks...
Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the developme...
typically obtained from biopsies, are not sufficiently large to support multi-omics analysis; (2) The majority of the tissue is composed of normal epithelia (NE),
Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequen
Using a data analysis workflow specifically for spatial transcriptomics, we identified genes that are significantly expressed in the supraoptic nuclei of both males and females (116 genes), primarily males (31 genes), and primarily females (73 genes). Genes enriched in the supraoptic nucleus of ...
Schematics of the spatial multi-omics technologies, analysis workflow, and example data. A Schematics of workflows for spatial transcriptomics, proteomics, and metabolic analyses. Upper: Spatial transcriptomics platforms are classified into those based on next-generation sequencing and those based on in ...
Most existing spatial transcriptome analysis methods are limited to single-slice spatial domain analysis, as they are unable to integrate gene expression and spatial information across multiple slices. However, with the advancement of spatial transcriptomics, it has become possible to obtain multiple slice...
Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistica...
Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a