SpatialRef: a reference of spatial omics with known spot annotation

Spatial omics technologies have enabled the creation of intricate spatial maps that capture molecular features and tissue morphology, providing valuable insights into the spatial associations and functional organization of tissues. Accurate annotation of spot or domain types is essential for downstream spatial omics analyses, but this remains challenging. Therefore, this study aimed to develop a manually curated spatial omics database (SpatialRef, https://bio.liclab.net/spatialref/), to provide comprehensive and high-quality spatial omics data with known spot labels across multiple species. The current version of SpatialRef aggregates >9 million manually annotated spots across 17 Human, Mouse and Drosophila tissue types through extensive review and strict quality control, covering multiple spatial sequencing technologies and >400 spot/domain types from original studies. Furthermore, SpatialRef supports various spatial omics analyses about known spot types, including differentially expressed genes, spatially variable genes, Gene Ontology (GO)/KEGG annotation, spatial communication and spatial trajectories. With a user-friendly interface, SpatialRef facilitates querying, browsing and visualizing, thereby aiding in elucidating the functional relevance of spatial domains within the tissue and uncovering potential biological effects.

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Publication Details

Journal
Nucleic Acids Research
Published
2024-10-17
DOI
https://doi.org/10.1093/nar/gkae892
Citations
6
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
1.40

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article

SpatialRef: a reference of spatial omics with known spot annotation

Binglong Li, Fengcui Qian, Desi Shang, Yuxiang Yan et al.
6 citations
Nucleic Acids Research
Single-cell and spatial transcriptomics
1.40
article

SpatialRef: a reference of spatial omics with known spot annotation

Binglong Li, Fengcui Qian, Desi Shang, Yuxiang Yan, Mingxue Yin, Tingting Yu, Han Zhang, Qiuyu Wang, Ting Cui, Chunquan Li, Jianing Zhang, Yanyu Li, Zi-Hao Hu, Qiao-Li Fang
article en
6 citations

Abstract

Spatial omics technologies have enabled the creation of intricate spatial maps that capture molecular features and tissue morphology, providing valuable insights into the spatial associations and functional organization of tissues. Accurate annotation of spot or domain types is essential for downstream spatial omics analyses, but this remains challenging. Therefore, this study aimed to develop a manually curated spatial omics database (SpatialRef, https://bio.liclab.net/spatialref/), to provide comprehensive and high-quality spatial omics data with known spot labels across multiple species. The current version of SpatialRef aggregates >9 million manually annotated spots across 17 Human, Mouse and Drosophila tissue types through extensive review and strict quality control, covering multiple spatial sequencing technologies and >400 spot/domain types from original studies. Furthermore, SpatialRef supports various spatial omics analyses about known spot types, including differentially expressed genes, spatially variable genes, Gene Ontology (GO)/KEGG annotation, spatial communication and spatial trajectories. With a user-friendly interface, SpatialRef facilitates querying, browsing and visualizing, thereby aiding in elucidating the functional relevance of spatial domains within the tissue and uncovering potential biological effects.

Nucleic Acids ResearchVol. 53(D1)
University of South China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hunan Province, University of South China
Openalex Percentile: Top 18%
Single-cell and spatial transcriptomics
1.40
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