Tutorials

Contents:

• Tumor contour with Xenium data
  • 1. Loading library
  • 2. Reading Xenium data
  • 3. Preprocessing
  • 4. Distance-based gene clustering
  • 5. Validation of gene expression on space
  • 6. Expression distribution at ROI
• Single TME analysis with Xenium data
  • 1. Loading library
  • 2. Reading preprocessed Xenium data
  • 3. Converting from grid data to single TME data
  • 4. Clustering
  • 5. Marker gene analysis
  • 6. UMAP
  • 7. Mapping cluster label to image
  • 8. Trajectory analysis
• SKNY_paper_Sakai_et_al
  • Figure 2. Detection of spatial domain with Xenium data accurately discriminates between the tumor and stromal region.
  • Figure 3. Clustering and annotation of spatial domain based on gene expressions.
  • Figure 4. Estimating spatial domain trajectory reveals temporal gene expression gradient along with cancer progression.
  • Figure 5. Spatial stratification of each spatial domain cluster elucidating endothelial cell invasion into the tumor.
  • Figure 6. Comparison of immune cells distribution in microenvironments between DCIS and IDC regions.
  • Figure 7. Cataloguing spatial domains using Xenium data of hepatic metastasis from colorectal cancer in TRIUMPH trial.