Fig 2. SparseDOSSA accurately recapitulates different microbial community structures.

We compared SparseDOSSA 2 simulated microbial counts versus those of three human microbiome training template datasets (Stool, Vaginal, and IBD). A) Bray-Curtis ordination shows global agreement between SparseDOSSA simulated microbial abundance profiles and those of their originating real-world populations. B) This was quantified by PERMANOVA R² statistics, showing that SparseDOSSA simulated samples were significantly less systematically differentiated from their targets than existing DM and metaSPARSim methods in almost all cases (Wilcoxon rank sum test p-values included in S3 Table). R² compared against randomly split original real-world data are included as baseline controls. C) Representative features from each environment are similarly distributed between real-world and SparseDOSSA simulated samples, as shown in empirical cumulative distribution functions (CDFs) of log-10 relative abundances (with pseudo value 1e-6 to visually represent zeros). D) Per-feature Kolmogorov-Smirnov summary statistics quantify that SparseDOSSA outperforms existing methods in simulating realistic feature-level relative abundance distributions. First, the similarity between the model-simulated feature abundance distribution versus that in the real-world dataset is quantified with K-S statistics. Then, the K-S statistics for SparseDOSSA and the other two models (DM and metaSPARSim) are plotted on the x- and y-axis, respectively (each point representing one feature, smaller K-S statistics represent better approximation). Lastly, the K-S statistics of SparseDOSSA versus other models are formally tested using Wilcoxon signed rank tests (p-values are significant and included in S4 Table).