Streamlining spatial omics data analysis with Pysodb
Advances in spatial omics technologies have improved the understanding of cellular
organization in tissues, leading to the generation of complex and heterogeneous data and …
organization in tissues, leading to the generation of complex and heterogeneous data and …
[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 …
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Spatial transcriptomics technologies generate gene expression profiles with spatial context,
requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample …
requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample …
SODB facilitates comprehensive exploration of spatial omics data
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 …
Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources …
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 …
Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our
understanding of tissue spatial architecture and biology. Although current ST methods …
understanding of tissue spatial architecture and biology. Although current ST methods …
MENDER: fast and scalable tissue structure identification in spatial omics data
Z Yuan - Nature Communications, 2024 - nature.com
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 …
increasingly complex models, such as Graph Neural Networks and Bayesian networks, are …
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 …
[HTML][HTML] A guidebook of spatial transcriptomic technologies, data resources and analysis approaches
L Yue, F Liu, J Hu, P Yang, Y Wang, J Dong… - Computational and …, 2023 - Elsevier
Advances in transcriptomic technologies have deepened our understanding of the cellular
gene expression programs of multicellular organisms and provided a theoretical basis for …
gene expression programs of multicellular organisms and provided a theoretical basis for …