Spatial Transcriptomics
Spatial transcriptomics is a cutting-edge technology that maps gene expression to precise locations within a tissue, allowing researchers to study how cells’ functions and interactions are organized in their native spatial context.
Reveiw and Research Papers
- Museum of spatial transcriptomics
- Exploring tissue architecture using spatial transcriptomics
- Spatial transcriptomics in health and disease
- An introduction to spatial transcriptomics for biomedical research
- Statistical and machine learning methods for spatially resolved transcriptomics data analysis
- Deep learning in spatially resolved transcriptomics: a comprehensive technical view
- A selective review of recent developments in spatially variable gene detection for spatial transcriptomics
- Benchmarking cell-type clustering methods for spatially resolved transcriptomics data
- TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses
- Spatial transcriptomic clocks reveal cell proximity effects in brain ageing
- STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics
- TUSCAN: Tumor segmentation and classification analysis in spatial transcriptomics
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
- Cell-type deconvolution methods for spatial transcriptomics
Packages
Seurat is a widely used R package for single-cell and spatial transcriptomics that provides powerful tools for data preprocessing, integration, clustering, and visualization to uncover cellular identities and spatial organization.