ArticleCancer research2025
spatialGE Is a User-Friendly Web Application That Facilitates Spatial Transcriptomics Data Analysis.
Article in Cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- The CD49b (ITGA2) collagen receptor excludes CD8iScience · 2026Article
- Spatial Transcriptomics and Dual Dye Mapping Identify Wnt-Driven BBB Protection in Endothelial EphA4-Deficiency.Research square · 2026Article
- transFusion: a novel comprehensive platform for integration analysis of single-cell and spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.PLoS computational biology · 2026Article
- DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments.Nucleic acids research · 2026Article
- Single-cell sequencing technology in renal cancer: insights into tumor biology and clinical application.Biomarker research · 2025Review
- Spatial Transcriptomics in Thyroid Cancer: Applications, Limitations, and Future Perspectives.Cells · 2025Review
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Authors and funding
13 authors.
Funding
Abstract
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10× Visium samples from a cohort of melanoma metastasis and NanoString CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community. Significance: The spatialGE web application enables user-friendly exploratory analysis of spatial transcriptomics data by using a point-and-click interface to guide users from data input to discovery of spatial patterns, facilitating hypothesis generation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.