Additional file 2 of A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions
Creators: name: Yang, Lu
name: Chen, Jun
Publisher: name: figshare
Year: 2022
Subjects: Molecular Biology
Biotechnology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified

description: Additional file 1: Figure S1. Basic steps of the proposed semiparametric simulation framework. Figure S2. The fit of the estimated beta mixture prior for several representative taxa in the COMBO (n = 98) dataset in comparison to the beta prior. Figure S3. The fit of the estimated beta mixture prior for several representative taxa in the American Gut Project (≈ 10, 000) dataset. Figure S4. P-value distributions based on 10,000 simulation runs (a) when the abundance of a rare taxon (0.4% relative abundance, 25% physical absence) is the same between two groups, (b) when the abundance of the same taxon (0.4% relative abundance, 25% physical absence) increases by 25% in one group. Figure S5. P-value distributions based on 10,000 simulation runs (a) when the abundance of an abundant taxon (9% relative abundance, 25% physical absence) is the same between two groups, (b) when the abundance of the same taxon (9% relative abundance, 25% physical absence) increases by 25% in one group. Figure S6. Comparison of the FDR control and power using different thresholds to select the reference set under (a) balanced and (b) unbalanced settings (settings 9 and 10 shown in Table 2). Figure S7. Comparison of sample- and taxon-level characteristics between the semiparametric approach and Dirichlet-multinomial (DM) model simulated data. Figure S8. Dirichlet-multinomial model tends to (a) overestimate the mean abundance for those less abundant taxa and (b) underestimate the mean abundance of those abundant taxa in vaginal data. Figure S9. Heat maps showing the relative abundance data generated by Dirichlet-multinomial model and the proposed semiparametric approach, in comparison to the real data for (a) stool and (b) vaginal. Figure S10. Performance of differential abundance analysis methods under the global null setting, visualized using bar plots corresponding to Fig. 1. Figure S11. Performance comparison of DESeq2 and edgeR using its native normalization method (RLE and TMM) and the GMPR normalization under the global null setting. Figure S12. Performance of differential abundance analysis methods under the balanced change setting for (a) stool and (b) vaginal data, and unbalanced change setting for (c) stool and (d) vaginal data (sample size = 100, taxa number = 500), visualized using bar plots corresponding to Fig. 2. Figure S13. Performance of differential abundance analysis methods under a small sample size (sample size = 50, taxa number = 500). (a) Balanced change setting, stool data. (b) Balanced change setting, vaginal data. (c) Unbalanced change setting, stool data. (d) Unbalanced change setting, vaginal data. Figure S14. Performance of differential abundance analysis methods under a small number of taxa (sample size = 100, taxa number = 50). Figure S15. Performance of ZicoSeq under the global null setting for stool and vaginal data with different numbers of samples and taxa. Figure S16. Comparison of ZicoSeq to the top-scoring method under different settings for stool and vaginal data. (a) Balanced and (b) unbalanced change setting (sample size = 100, taxa number = 500). (c) Balanced and (d) unbalanced change setting (sample size = 50, taxa number = 50). Figure S17. Comparison of ZicoSeq to the top-scoring method in Fig. S16ab under different settings for stool and vaginal data at the sample size of 1000. Figure S18. Performance of ZicoSeq in the presence of confounders under balanced change setting for (a) stool, (b) vaginal data, and unbalanced change setting for (c) stool, (d) vaginal data (sample size = 100, taxa number = 500), visualized using bar plots corresponding to Fig. 4. Figure S19. Performance of ZicoSeq when the sequencing depth differs by 4-fold between the groups, visualized using bar plots corresponding to Fig. 5. Figure S20. Performance of ZicoSeq when the sequencing depth differs by 9-fold between the groups. Figure S21. Run times (x86_64-pc-linux-gnu (64-bit) Red Hat Enterprise Linux Server 7.9, Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, 8GB running memory) of the evaluated differential abundance analysis methods over simulation runs (unbalanced setting, vaginal data, 100 samples and 500 taxa). Figure S22. Run times (x86_64-pc-linux-gnu (64-bit) Red Hat Enterprise Linux Server 7.9, Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, 8GB running memory) of ZicoSeq over simulation runs when sample size increases to 1000 and 5000 (unbalanced setting, vaginal data, 500 taxa). Figure S23. Box plots showing the distribution of Spearman correlation of p-values between no filtered datasets and filtered datasets (prevenance less than 40% or minimal abundance less than 0.002 are excluded for analysis) based on unbalanced change setting for vaginal data. Figure S24. The average percentage of significant taxa at 5% FDR of the 106 real datasets when the group labels are randomly shuffled. Figure S25. Ensemble methods at a consensus level of 20%, 40%, 60% and 80% (denoted as “pct20”, “pct40”, “pct60” and “pct80”). Figure S26. Performance comparison to recently developed methods - LinDA, fastANCOM and ZINQ under settings 2&amp;6. (a) Balanced, stool, (b) Balanced, vaginal, (c) Unbalanced Stool, (d) Unbalanced, vaginal. Table S1. Normalization methods reviewed in this study. Table S2. Package version and source link for the differential abundance analysis methods evaluated in this study. Table S3. Performance scoring system. Table S4. The evaluation metrics used in the performance summary. Table S5. Details of the experimental datasets