Additional file 1 of PyClone-VI: scalable inference of clonal population structures using whole genome data

Additional file 1. Table S1: Performance results for the comparison of PyClone and PyClone-VI using synthetic data used in Fig. 1. Table S2: Performance results for the comparison of PyClone and PyClone-VI using synthetic data used in Additional file 2. Table S3: Performance results for the comparison of PyClone and PyClone-VI using synthetic data used in Additional file 3. Table S4: Performance results for the comparison of PyClone and PyClone-VI using synthetic data used in Additional file 4. Table S5: Summary statistics for datasets used in Fig. 2. Table S6: Performance results for the analysis of DREAM SMC-HET data used in Fig. 2. Table S7: Friedman test results for comparing methods using the DREAM SMC-HET data. Table S8: Post-hoc Nemenyi test for comparing methods using the DREAM SMC-HET data. Table S9: Results from the PCAWG data analysis used in Fig. 3. Table S10: Predicted run time to analyze PCAWG data for programs used in DREAM analysis. Table S11: Results from the TRACERx data analysis used in Fig. 4.