scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks
The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized
the identification of cell types and the study of cellular states at a single-cell level. Despite its …
the identification of cell types and the study of cellular states at a single-cell level. Despite its …
Contrastively generative self-expression model for single-cell and spatial multimodal data
Advances in single-cell multi-omics technology provide an unprecedented opportunity to
fully understand cellular heterogeneity. However, integrating omics data from multiple …
fully understand cellular heterogeneity. However, integrating omics data from multiple …
Spatiotemporal Omics-Refining the landscape of precision medicine
Current streamline of precision medicine uses histomorphological and molecular
information to indicate individual phenotypes and genotypes to achieve optimal outcome of …
information to indicate individual phenotypes and genotypes to achieve optimal outcome of …
Graph deep learning enabled spatial domains identification for spatial transcriptomics
Advancing spatially resolved transcriptomics (ST) technologies help biologists
comprehensively understand organ function and tissue microenvironment. Accurate spatial …
comprehensively understand organ function and tissue microenvironment. Accurate spatial …
Spatially aware self-representation learning for tissue structure characterization and spatial functional genes identification
Spatially resolved transcriptomics (SRT) enable the comprehensive characterization of
transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial …
transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial …
Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network
Y Huo, Y Guo, J Wang, H Xue, Y Feng, W Chen… - Journal of Genetics and …, 2023 - Elsevier
Recent advances in spatially resolved transcriptomic technologies have enabled
unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial …
unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial …
[HTML][HTML] Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network
X Li, W Huang, X Xu, HY Zhang, Q Shi - Frontiers in Genetics, 2023 - frontiersin.org
Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to
investigate the complex and heterogeneous tissue organization. However, it is challenging …
investigate the complex and heterogeneous tissue organization. However, it is challenging …
[HTML][HTML] Pianno: a probabilistic framework automating semantic annotation for spatial transcriptomics
Y Zhou, W He, W Hou, Y Zhu - Nature Communications, 2024 - nature.com
Spatial transcriptomics has revolutionized the study of gene expression within tissues, while
preserving spatial context. However, annotating spatial spots' biological identity remains a …
preserving spatial context. However, annotating spatial spots' biological identity remains a …
Vesalius: high‐resolution in silico anatomization of spatial transcriptomic data using image analysis
Abstract Characterization of tissue architecture promises to deliver insights into
development, cell communication, and disease. In silico spatial domain retrieval methods …
development, cell communication, and disease. In silico spatial domain retrieval methods …
Mapping the topography of spatial gene expression with interpretable deep learning
Spatially resolved transcriptomics technologies provide high-throughput measurements of
gene expression in a tissue slice, but the sparsity of this data complicates the analysis of …
gene expression in a tissue slice, but the sparsity of this data complicates the analysis of …