16 papers · ranked by Valyu relevance
Helena L. Crowell, Yixing Dong, Ilaria Billato, Peiying Cai + 30 more
Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows…
Junchao Zhu, Ruining Deng, Junlin Guo, Tianyuan Yao + 13 more
Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation…
Kalen Clifton, Vivien Jiang, Rafael dos Santos Peixoto, Srujan Singh + 3 more
Comparative analysis of spatial transcriptomics (ST) data is needed to identify genes that spatially change in their expression patterns between conditions, such as in diseased versus healthy tissues. Existing methods, including those developed for and adapted from non-spatial transcriptomics, generally focus on…
Mohammad Faiz Iqbal Faiz, Elliot Jokl, Rachel Jennings, Karen Piper Hanley + 3 more
Spatial transcriptomics is rapidly advancing toward single cell level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical…
Lixia Chen Wu, Xinyu Hu, Fengwei Zhan, Chuhanwen Sun + 6 more
Sequencing-based spatial transcriptomics technologies, including Visium HD and Stereo-seq, now enable transcriptome-wide profiling at subcellular resolution. However, these platforms generate measurements over spatially barcoded units rather than biologically segmented cells, creating a fundamental bottleneck for…
An Wang, Donald Geman, Uthsav Chitra, Laurent Younes
Spatial transcriptomics (ST) technologies measure gene expression at thousands of locations within a two-dimensional tissue slice, enabling the study of spatial gene expression patterns. Spatial variation in gene expression is characterized by spatial gradients, or the collection of vector fields describing the…
Hao Wen, Qunlun Shen, Shuqin Zhang
Spatial transcriptomics enables comprehensive characterization of tissue architecture, and the identification of spatially variable genes (SVGs) is a critical step for defining region-specific molecular markers and uncovering spatially regulated mechanisms across diverse biological contexts. However, most existing…
Jordan J. Smith, Xi Wang, Matthew McPheeters, Made Airanthi Widjaja-Adhi + 4 more
Spatial transcriptomics enables high-resolution mapping of gene expression in intact tissues but remains challenging due to complex computational workflows that limit accessibility and reproducibility. Here, we present a Model Context Protocol (MCP) framework enabling natural language-driven spatial transcriptomics…
Weiqi Li, Xinzhou Ge, Yuan Jiang
Identifying spatially variable genes within individual cell types is essential for characterizing spatially organized cell states and microenvironments from spatial transcriptomics data. Several computational methods have been developed for identifying cell-type-specific spatially variable genes (ctSVGs), but their…
Qingyue Wang, Anqi Gao, Yuying Li, Parth Khatri + 5 more
Spatial transcriptomics data are largely available with RNA expression alone, limiting the detection of cell states defined by surface protein abundance. The lack of multi-omics spatial data limits the ability to identify immune cells and their signaling in the tumor microenvironment, as most solid tumors are…
Zhen-Hao Guo, De-Shuang Huang, Shihua Zhang
The rapid advancement of spatial omics technology has revolutionized biomedical research, unveiling unprecedented insights into the molecular and cellular architecture of biological systems. Despite this progress, a critical limitation arises from the widespread use of disparate analytical tools within a single…
Macrina Lobo, Ziqi Zhang, Xiuwei Zhang
Spot-based spatial transcriptomics (ST) captures aggregated transcriptomic profiles at spatial locations (spots) in tissue slices. Cell type deconvolution methods decode each spot and estimate the proportion of every cell type in the spot, necessary for uncovering spatial cell type distributions for further downstream…
Parth Khatri, Michael A Newton, Christina Kendziorski, Huy Q. Dinh
Spatial transcriptomics has enabled finer-grained analyses of cell-cell interactions through the co-expression of ligands and their cognate receptors, thereby accounting for the spatial constraints of signaling. However, existing methods employ random permutations or analytic calculations to assess statistical…
Lambda Moses, Aurelie Herault, Lauriane Cabon, Bianca Dumitrascu
Spatial biology spans multiple length scales, from intracellular organization to tissue-level architecture. Spatial transcriptomics captures this structure, yet most analyses operate at a single spatial resolution, implicitly assuming that biological organization is scale-consistent. In practice, spatial…
Puneet Velidi, Zhengxiao Wei, Farouk S. Nathoo
Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. Covariance non-stationarity has long been recognized in spatial statistics as an important feature of spatial data, yet it has received little attention in spatial transcriptomics. We show that this omission…
Yannick Mahlich, Harkirat Sohi, Marija Velickovic, Paul Piehowski + 2 more
Spatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance…