ArticleFrontiers in neuroscience2022
Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- spaGFM is a scalable graph foundation model for spatial transcriptomics analyses.Research square · 2026Article
- Article
- Shared transcriptomic signatures in perilesional and contralesional cortex after ischemic stroke.Journal of neuroinflammation · 2026Article
- Shared Transcriptomic Signatures in Perilesional and Contralesional Cortex.bioRxiv : the preprint server for biology · 2026Article
- Spatial analysis of HPV-associated cervical intraepithelial neoplastic tissues demonstrate distinct immune signatures associated with cervical cancer progression.The Journal of pathology · 2026Article
- Article
- Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.Nature communications · 2024Article
- Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models.Scientific reports · 2024Article
- Application of spatial transcriptome technologies to neurological diseases.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Spatial transcriptomics (STs) simultaneously obtains the location and amount of gene expression within a tissue section. However, current methods like FindMarkers calculated the differentially expressed genes (DEGs) based on the classical statistics, which should abolish the spatial information. Materials and methods: A new method named spatial analysis of spatial transcriptomics (saSpatial) was developed for both the location and the amount of gene expression. Then saSpatial was applied to detect DEGs in both inter- and intra-cross sections. DEGs detected by saSpatial were compared with those detected by FindMarkers. Results: Spatial analysis of spatial transcriptomics was founded on the basis of spatial statistics. It was able to detect DEGs in different regions in the normal brain section. As for the brain with ischemic stroke, saSpatial revealed the DEGs for the ischemic core and penumbra. In addition, saSpatial characterized the genetic heterogeneity in the normal and ischemic cortex. Compared to FindMarkers, a larger number of valuable DEGs were found by saSpatial. Conclusion: Spatial analysis of spatial transcriptomics was able to effectively detect DEGs in STs data. It was a simple and valuable tool that could help potential researchers to find more valuable genes in the future research.
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