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 …

Background: Spatial transcriptomics technologies fully utilize spatial location information,
tissue morphological features, and transcriptional profiles. Integrating these data can greatly …

With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple
ST datasets from different conditions, technologies and developmental stages is becoming …

The rapid development of spatial transcriptomics allows the measurement of RNA
abundance at a high spatial resolution, making it possible to simultaneously profile gene …

Spatially resolved transcriptomics provides the opportunity to investigate the gene
expression profiles and the spatial context of cells in naive state, but at low transcript …

Tissue structure identification is a crucial task in spatial omics data analysis, for which
increasingly complex models, such as Graph Neural Networks and Bayesian networks, are …

Advances in spatial omics technologies have improved the understanding of cellular
organization in tissues, leading to the generation of complex and heterogeneous data and …

The rapidly developing spatial omics generated datasets with diverse scales and modalities.
However, most existing methods focus on modeling dynamics of single cells while ignore …

Motivation: Recent advances in spatially resolved transcriptomics (ST) technologies enable
the measurement of gene expression profiles while preserving cellular spatial context …

Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of
spatial heterogeneity and increase the demand for comprehensive methods to effectively …