Supplementary Data Figures S1-S14 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

Figure S1. Details of animal model and fusion gene CM, Figure S2. Ex vivo flow cytometry analysis of bone marrow, Figure S3. Details of principal component analysis, Figure S4. Comparison of state-space construction with different dimension reduction methods, Figure S5. Hierarchical clustering of time-series RNA-seq data and relation to state-space, Figure S6. Details of critical point estimation, construction of quasi-potential, and state-space dynamics, Figure S7. Correlation of state-space geometry with Kit and CM gene expression. Sensitivity of state-space geometry to inclusion of Kit and CM, Figure S8. Volcano plots of critical-point based differential gene expression analysis, Figure S9. Heatmaps of selected GO pathways in early, transition, persistent, and leukemic events, Figure S10. Computation of eigengene angle in state-space, Figure S11. Details of principal component analysis of validation cohorts and state-space dynamics, Figure S12. Mean-squared displacement analysis of particle trajectories in state-space and calibration of Fokker-Planck diffusion coefficient with training cohort, Figure S13. Sensitivity analysis of state-space construction to sample and normalization thresholds, Figure S14. Bootstrap cross validation of state-space construction.