[HTML][HTML] A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics T. Liu et al. Overview of Spatial Transcriptomics …
Spatial transcriptomics technologies enable researchers to accurately quantify and localize
messenger ribonucleic acid (mRNA) transcripts at a high resolution while preserving their …
messenger ribonucleic acid (mRNA) transcripts at a high resolution while preserving their …
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues
Spatial transcriptomics (ST) technologies generate multiple data types from biological
samples, namely gene expression, physical distance between data points, and/or tissue …
samples, namely gene expression, physical distance between data points, and/or tissue …
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Spatial omics data are clustered to define both cell types and tissue domains. We present
Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an …
Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an …
Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
P Kiessling, C Kuppe - Genome Medicine, 2024 - Springer
Spatial multi-omic studies have emerged as a promising approach to comprehensively
analyze cells in tissues, enabling the joint analysis of multiple data modalities like …
analyze cells in tissues, enabling the joint analysis of multiple data modalities like …
Benchmarking spatial clustering methods with spatially resolved transcriptomics data
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 …
scope of tissue physiology studies from cell-centroid to structure-centroid with spatially …
Stgnnks: identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering
Background: Spatial transcriptomics technologies fully utilize spatial location information,
tissue morphological features, and transcriptional profiles. Integrating these data can greatly …
tissue morphological features, and transcriptional profiles. Integrating these data can greatly …
Spatial-linked alignment tool (SLAT) for aligning heterogenous slices
Spatially resolved omics technologies reveal the spatial organization of cells in various
biological systems. Here we propose SLAT (Spatially-Linked Alignment Tool), a graph …
biological systems. Here we propose SLAT (Spatially-Linked Alignment Tool), a graph …
Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks
Motivation: Recent advances in spatially resolved transcriptomics (ST) technologies enable
the measurement of gene expression profiles while preserving cellular spatial context …
the measurement of gene expression profiles while preserving cellular spatial context …
SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
Properly integrating spatially resolved transcriptomics (SRT) generated from different
batches into a unified gene-spatial coordinate system could enable the construction of a …
batches into a unified gene-spatial coordinate system could enable the construction of a …
Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism
Motivation Recent advances in spatial transcriptomics technologies have enabled gene
expression profiles while preserving spatial context. Accurately identifying spatial domains is …
expression profiles while preserving spatial context. Accurately identifying spatial domains is …