The rapid development of spatial transcriptomics (ST) techniques has allowed the
measurement of transcriptional levels across many genes together with the spatial positions …

The development of spatial transcriptomics (ST) technologies has transformed genetic
research from a single-cell data level to a two-dimensional spatial coordinate system and …

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 …

Recent advances in spatially resolved transcriptomics have enabled comprehensive
measurements of gene expression patterns while retaining the spatial context of the tissue …

Spatial transcriptomics technologies generate gene expression profiles with spatial context,
requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample …

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities
to understand tissue organization and function in spatial context. However, it is still …

Spatial omics technologies generate wealthy but highly complex datasets. Here we present
Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources …

One major challenge in analyzing spatial transcriptomic datasets is to simultaneously
incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce …

Spatial transcriptomics data can provide high-throughput gene expression profiling and the
spatial structure of tissues simultaneously. Most studies have relied on only the gene …

Spatial clustering, which shares an analogy with single-cell clustering, has expanded the
scope of tissue physiology studies from cell-centroid to structure-centroid with spatially …