| Autor: |
Ospina OE; Moffitt Cancer Center, Tampa, FL, United States., Manjarres-Betancur R; Moffitt Cancer Center, Tampa, FL, United States., Gonzalez-Calderon G; Moffitt Cancer Center and Research Institute, United States., Soupir AC; Moffitt Cancer Center, Tampa, FL, United States., Smalley I; Moffitt Cancer Center, TAMPA, FL, United States., Tsai KY; Moffitt Cancer Center, Tampa, FL, United States., Markowitz J; Moffitt Cancer Center, Tampa, Florida, United States., Vallebuona E; Moffitt Cancer Center, Tampa, FL, United States., Berglund AE; Moffitt Cancer Center, Tampa, FL, United States., Eschrich SA; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States., Yu X; Moffitt Cancer Center, Tampa, FL, United States., Fridley BL; Children's Mercy Hospital, Kansas City, MO, United States. |
| Abstrakt: |
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 of 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: 10X 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. |
