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spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.
Ospina, Oscar E; Manjarres-Betancur, Roberto; Gonzalez-Calderon, Guillermo; Soupir, Alex C; Smalley, Inna; Tsai, Kenneth; Markowitz, Joseph; Vallebuona, Ethan; Berglund, Anders; Eschrich, Steven; Yu, Xiaoqing; Fridley, Brooke L.
  • Ospina OE; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
  • Manjarres-Betancur R; Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL, USA.
  • Gonzalez-Calderon G; Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL, USA.
  • Soupir AC; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
  • Smalley I; Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA.
  • Tsai K; Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA.
  • Markowitz J; Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL, USA.
  • Vallebuona E; Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA.
  • Berglund A; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
  • Eschrich S; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
  • Yu X; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
  • Fridley BL; Division of Health Services and Outcomes Research, Children's Mercy, Kansas City, MO, USA.
bioRxiv ; 2024 Jul 02.
Article en En | MEDLINE | ID: mdl-39005315
ABSTRACT
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides 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 enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.
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