Additional file 1 of Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems
Creators: name: Hong, Renhao
name: Tong, Yuyan
name: Liu, Huisheng
name: Chen, Pei
name: Liu, Rui
Publisher: name: figshare
Year: 2024
Subjects: Medicine
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer
Computational Biology

description: Additional file 1: Table S1. Traditional gene biomarkers used to detect critical points in each dataset. Table S2. The cancer-related signaling pathways enriched by gene pairs with high entropy in critical stage of each dataset. Table S3. Genes included in the network of each dataset. Table S4. Summary sheets for positive and negative edges of TCGA datasets. Table S4.1. Summary sheets for positive and negative edges of COAD. Table S4.2. Summary sheets for positive and negative edges of LUAD. Table S4.3. Summary sheets for positive and negative edges of THCA and KIRC. Table S5. Performance statistics of ERE in various datasets based on the bootstrapping strategy. Figure S1. Three states during disease progression. Figure S2. Identifying the critical stage for KIRP. Figure S3. A schematic illustration for validating the identified critical state. Figure S4. Validating the identified critical states of THCA and KIRP. Figure S5. The performance of ERE under different sample sizes in numerical simulation. Figure S6. Comparison of the performance of ERE under different noise strengths in numerical simulation with other methods. Figure S7. Cancer development regulatory mechanisms revealed by ERE signaling gene pairs in KIRC. Figure S8. Survival analysis based on positive and negative edge biomarkers for COAD, LUAD, THCA and KIRC. Figure S9. A model of 16-nodes network for numerical simulation. Figure S10. Probability density functions fit by kernel density estimation based on normal and case samples. Figure S11. Comparison of the performances of the ERE method and traditional biomarkers. Figure S12. Dynamic evolution of the networks across all stages in each tumor. Figure S13. Comparison of the prognosis results based on the identified critical stages by the ERE method and DEGs for THCA. Figure S14. Performance of ERE in numerical simulation. Figure S15. The performance of ERE on the COAD, LUAD, THCA, and KIRC using a bootstrapping strategy. Figure S16. An illustration of the “after-transition state”. Figure S17. The three stages transition of interactions between certain genes during the entire disease progression. Figure S18. The comparison of the performance of ERE with the standard relative entropy. Figure S19. Box plot of ERE values for acute lung injury. Figure S20. The performance of ERE under different groups of edges with highest ERE values. Figure S21. Comparison of the performance of ERE with that of pure physical approaches. Note S1. Details of kernel density estimation. Note S2. Details of numerical simulation. Note S3. One-sample t-test. Note S4. Summary for positive and negative edges of TCGA datasets. Note S5. Verification for the identified critical state. Note S6. ERE gene pairs affect the Rap1 signaling pathway in tumor progression. Note S7. KEGG pathway enrichment analysis. Note S8. Theoretical basis. Note S9. Performance of ERE in numerical simulation. Note S10. Details for the expression calculation of DEGs. Note S11. Details for the applied issue of ERE. References S1.