Single-cell transcriptomics has broadened our understanding of cellular diversity and gene
expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has …

Over the past decade, single-cell genomics technologies have allowed scalable profiling of
cell-type-specific features, which has substantially increased our ability to study cellular …

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics,
spatial proteomics, and computational approaches to define glioma cellular states and …

Spatial regulation of RNA plays a critical role in gene expression regulation and cellular
function. Understanding spatially resolved RNA dynamics and translation is vital for bringing …

Background Spatial transcriptomics (ST) is advancing our understanding of complex tissues
and organisms. However, building a robust clustering algorithm to define spatially coherent …

Spatial landmarks are crucial in describing histological features between samples or sites,
tracking regions of interest in microscopy, and registering tissue samples within a common …

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes
(SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and …

The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research,
offering unprecedented potential to enhance cancer diagnosis and treatment. This …

With the flourishing of spatial omics technologies, alignment and stitching of slices becomes
indispensable to decipher a holistic view of 3D molecular profile. However, existing …

Motivation Spatial omics data demand computational analysis but many analysis tools have
computational resource requirements that increase with the number of cells analyzed. This …