SODB facilitates comprehensive exploration of spatial omics data

Yuan, Zhiyuan; Pan, Wentao; Zhao, Xuan; Zhao, Fangyuan; Xu, Zhimeng; Li, Xiu; Zhao, Yi; Zhang, Michael Q.; Yao, Jianhua

doi:10.1038/s41592-023-01773-7

Cited by 62 publications

(38 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While tools for working with z-stacked SRT datasets in 3D such as STich3D 30 are available, it is more limited in its functionality than VR-Omics. It has currently only demonstrated compatibility with sequencingbased SRT platforms and does not offer a GUI 20 . Users will require knowledge of Python to run the bioinformatics analysis of datasets prior to visualisation within STich3D, a task handled in its entirety by the AW in VR-Omics.…”

Section: Data Analysis In 3dmentioning

confidence: 99%

“…2b). This expands on the capabilities of SODB as the user is not limited to what is provided by a matched database 20 .…”

Section: Cross-srt Platform Comparisons and Performancementioning

confidence: 99%

“…These are often proprietary (Visium Loupe Browser 14 , Xenium Explorer 15 , MERSCOPE Web Vizualiser 16 ) and sometimes subscription-based (AtoMX 17 ). Databases warehousing SRT experiments also offer useful portals for the mining and exploration of existing SRT data (SpatialDB 18 , STOmicsDB 19 , SODB 20 ). However, no platform currently exists as a one-stop shop to process, analyse and visualise SRT data directly from the manufacturer to a visualisation interface, that is compatible with all SRT technologies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Integration of Multi-Slice Spatial Transcriptomics Data in 2D and 3D

Bienroth

Charitakis

Jaeger-Honz

et al. 2023

Preprint

View full text Add to dashboard Cite

Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible. VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html

show abstract

Section: Data Analysis In 3dmentioning

confidence: 99%

“…2b). This expands on the capabilities of SODB as the user is not limited to what is provided by a matched database 20 .…”

Section: Cross-srt Platform Comparisons and Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Integration of Multi-Slice Spatial Transcriptomics Data in 2D and 3D

Bienroth

Charitakis

Jaeger-Honz

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…All data and code required to replicate the analyses, including spaceranger output files, H&E images and additional files will be available for download at Mendeley data. For interactive exploration, the spatial data will be available in Spatial Omics DataBase (SODB, https://gene.ai.tencent.com/SpatialOmics/) 86 . Sequence data for all samples presented in this study will be available upon publication.…”

Section: Data Availabilitymentioning

confidence: 99%

Spatiotemporal transcriptomic map of ischemic brain injury

Žucha

Abaffy

Kirdajová

et al. 2023

Preprint

View full text Add to dashboard Cite

The role of non-neuronal cells in the resolution of cerebral ischemia remains to be fully understood. To decode key cellular processes that occur after ischemia, we performed spatial and single-cell transcriptomic profiling of mouse brain tissue during the first week of injury. Cortical gene expression was severely disrupted, being defined by inflammation and cell death in the lesion core, and glial scar formation on the periphery. For each of the three major glial populations, an inflammatory-responsive state, resembling the reactive states observed in neurodegenerative contexts, was documented. The recovered spectrum of ischemia-induced oligodendrocyte states supports the emerging hypothesis that oligodendrocytes actively respond to and modulate the neuroinflammatory stimulus. Thus, we present a landmark transcriptomic dataset that provides a comprehensive view of spatiotemporal organization of processes in the post-ischemic brain and documents the conservation of glial response in CNS pathology.

show abstract

“…The SRT input data containing the gene expression (GEX) matrix and spatial coordination matrix is encoded into a graph structure, where edges are determined by the spatial relationships between cells, and each node is associated with the binarized gene expression vector (see Methods). Through unsupervised training, BINARY generates embeddings for all nodes within this graph, which are subsequently utilized for clustering to delineate spatial domains and for color space embedding to visualize tissue landscape 23 (see Methods). BINARY’s workflow is illustrated in Figure 1A.…”

Section: Main Articlementioning

confidence: 99%

Spatially Resolved Gene Expression is Not Necessary for Identifying Spatial Domains

Lin,

Zhao,

Yuan

2023

Preprint

Self Cite

View full text Add to dashboard Cite

The development of Spatially Resolved Transcriptomics (SRT) technologies has revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, named "BINARY," relying exclusively on binarized SRT data to delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

show abstract

SODB facilitates comprehensive exploration of spatial omics data

Cited by 62 publications

References 96 publications

Automated Integration of Multi-Slice Spatial Transcriptomics Data in 2D and 3D

Automated Integration of Multi-Slice Spatial Transcriptomics Data in 2D and 3D

Spatiotemporal transcriptomic map of ischemic brain injury

Spatially Resolved Gene Expression is Not Necessary for Identifying Spatial Domains

Contact Info

Product

Resources

About