Additional file 1 of STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data
Creators: name: Bae, Sungwoo
name: Lee, Hyekyoung
name: Na, Kwon Joong
name: Lee, Dong Soo
name: Choi, Hongyoon
name: Kim, Young Tae
Publisher: name: figshare
Year: 2025
Subjects: subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: Plant Biology
subject: FOS: Biological sciences

description: Additional file 1: Supplementary Figures. Fig. S1. Schematic image for converting image-based SRT to grid-based data. Fig. S2. Extracting CCs by Morse filtration and dendrogram smoothing in Visium dataset. Fig. S3. Parameter optimization for STopover. Fig. S4. Aggregated CCs obtained across various parameters in simulated dataset. Fig. S5. Calculation of weighted Jaccard index from the extracted CC pairs in the simulation dataset. Fig. S6. Spatial distribution of tS2 and other main cell types composing the lung cancer tissue in barcode-based SRT. Fig. S7. The location of the top CC pairs between tS2 and NK/T cells in a PD-L1 high lung cancer tissue. Fig. S8. Colocalization patterns between tS2 and multiple T cell subtypes in barcode-based SRT of lung cancer. Fig. S9. Scatter plots between pseudobulk RNA expression and Jcomp value of tS2 and NK/T cells. Fig. S10. Comparison of set-based and weighted Jaccard index across 11 barcode-based SRT of lung cancer. Fig. S11. Spatial expression of the top 3 ligand-receptor pairs in PD-L1 high lung cancer tissue. Fig. S12. The spatial overlap patterns of the top ligand-receptorpairs constrained to the tS2 or myeloid cell domain in PD-L1 high lung cancer tissue. Fig. S13. The common top ligand-receptorinteractions in Clusters 1 and 2 defined by infiltration patterns of immune and stromal cells to tumor cells. Fig. S14. The top ligand-receptorinteractions estimated by STopover using the Omnipath database. Fig. S15. The spatial overlap patterns of the T cell action-related LR pairs constrained to the tS2 and NK/T cell colocalized domain in PD-L1 high lung cancer tissue. Fig. S16. Spatial distribution of tS2 and other main cell types composing the lung cancer tissue in image-based SRT. Fig. S17. The colocalization patterns between tS2 and multiple T cell subtypes in image-based SRT of lung cancer. Fig. S18. Spatial distribution of the top ligand-receptor expression specific to tS2 and NK/T cells. Fig. S19. The top differentially upregulated LR pairs and related Gene Ontologyterms in ER+ or TNBC tissues. Fig. S20. The spatial overlap patterns of the top differential upregulated LR pairs in ER+ or TNBC breast tissues. Fig. S21. Investigation of spatial heterogeneity of the lung cancer using ATHENA and spatialGE. Fig. S22. Investigation of spatial heterogeneity in lung cancer using SpaCET and stLearn. Fig. S23. Evaluation of a method for estimating cell type-specific LR interaction in barcode-based SRT. Fig. S24. Colocalization patterns predicted by STopover using different cell type deconvolution and annotation methods. Fig. S25. STopover analysis before and after correction for RNA diffusion