Additional file 1 of SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Creators: name: Zhu, Jiaqiang
name: Shang, Lulu
name: Zhou, Xiang
Publisher: name: figshare
Year: 2024
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 1: Fig. S1. An overview of the Shiny-app component of SRTsim for reference-free simulations. Fig. S2. Benchmarking SRTsim against eight existing scRNA-seq simulators for generating SRT data based on seven SRT data by 10x Visium. Fig. S3. Benchmarking SRTsim against eight existing scRNA-seq simulators for generating SRT data based on eight DLPFC SRT data by 10x Visium. Fig. S4. Benchmarking SRTsim against eight existing scRNA-seq simulators for generating SRT data based on six SRT data by DBiT-seq and one SRT data by ST. Fig. S5. Benchmarking SRTsim against eight existing scRNA-seq simulators for generating SRT data based on SRT data by single-cell resolution SRT techniques. Fig. S6. Scatter plots comparing Moran’s I in the synthetic data generated by different simulators versus that in the reference data. Fig. S7. Benchmarking SRTsim against scDesign2 and SymSim for generating SRT data with a different number of locations. Fig. S8. Computational cost of different simulators for generating synthetic data based on references from different spatial transcriptomics techniques. Fig. S9. Distributions of six metrics and Moran’s I for the simulation based on the reference DLPFC sample 151673. Fig. S10. Spatial distributions of the domain assignments by six spatial clustering methods on the synthetic data generated by SRTsim in the tissue-based simulations. Fig. S11. Spatial distributions of domain assignments by six spatial clustering methods on the synthetic data generated by SRTsim in the domain-specific simulations. Fig. S12. Distributions of six metrics for the simulations based on STARmap data. Fig. S13. Spatial distributions of domain assignments by six spatial clustering methods based on the synthetic data generated by SRTsim via the tissue-based simulation. Fig. S14. Spatial distributions of domain assignments by six spatial clustering methods based on the synthetic data generated by SRTsim via the domain-specific simulation. Fig. S15. The performance of stLearn with Louvain measured by the adjusted Rand index is evaluated under simulations with varying-sequencing-depth (y-axis) and varying-location-number (x-axis). Fig. S16. Comparisons between the reference and synthetic data generated by SRTsim. Fig. S17. Benchmarking spatial expression analysis methods using synthetic data from SRTsim based on the DLPFC reference. Fig. S18. Benchmarking spatial expression analysis methods using synthetic data from SRTsim based on the STARmap reference. Fig. S19. Benchmarking cell-cell communication identification methods using synthetic data from SRTsim (precision). Fig. S20. Benchmarking cell-cell communication identification methods using synthetic data from SRTsim (recall rate). Fig. S21. Benchmarking cell-cell communication identification methods using synthetic data from SRTsim (specificity). Table S1. Summary of 49 SRT datasets used in the present study. Table S5. Computational stability of all simulators. X represents failure, while – represents success. Table S6. Total number of alternatives in SE analysis.