[HTML][HTML] A comprehensive overview of graph neural network-based approaches to clustering for 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 …
Systematic comparison of sequencing-based spatial transcriptomic methods
Y You, Y Fu, L Li, Z Zhang, S Jia, S Lu, W Ren, Y Liu… - Nature …, 2024 - nature.com
Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed
important advancements by facilitating transcriptome-scale spatial gene expression …
important advancements by facilitating transcriptome-scale spatial gene expression …
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 …
Spatial transcriptomics data and analytical methods: an updated perspective
S Khan, JJ Kim - Drug Discovery Today, 2024 - Elsevier
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging
and transcriptomic data to enable the high-throughput analysis of the spatial localization of …
and transcriptomic data to enable the high-throughput analysis of the spatial localization of …
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 …
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 …
Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics
Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of
spatial heterogeneity and increase the demand for comprehensive methods to effectively …
spatial heterogeneity and increase the demand for comprehensive methods to effectively …
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Background Spatial transcriptomics (ST) is advancing our understanding of complex tissues
and organisms. However, building a robust clustering algorithm to define spatially coherent …
and organisms. However, building a robust clustering algorithm to define spatially coherent …
PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
Y Liang, G Shi, R Cai, Y Yuan, Z Xie, L Yu… - Nature …, 2024 - nature.com
Computational methods have been proposed to leverage spatially resolved transcriptomic
data, pinpointing genes with spatial expression patterns and delineating tissue domains …
data, pinpointing genes with spatial expression patterns and delineating tissue domains …
stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation
Background Deciphering spatial domains using spatially resolved transcriptomics (SRT) is
of great value for characterizing and understanding tissue architecture. However, the …
of great value for characterizing and understanding tissue architecture. However, the …