Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data: <b>Supplemental Information</b>

Here, we publish supplemental information for our manuscript entitled "<b>Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data</b>" that we are gointg to submitte to F1000-Research (first submission stage).The study protocol is the following: Kohnert, E., &amp; Kreutz, C. (2025). Computational study protocol: leveraging synthetic data to validate a benchmark study for differential abundance tests for 16s microbiome sequencing data. <i>F1000Research</i>, <i>13</i>, 1180. https://f1000research.com/articles/13-1180The code used to generate this is available at https://github.com/kreutz-lab/ValidationBenchmarkStudyF1000<b>Short description of the files:</b>S_Fig_1: Boxplots indicating which data templates were removed from analysis due to unrealistic simulations.S_Fig_2: Boxplots showing the accuracy of all data characteristics for sparseDOSSA2-generated data.S_Fig_3: PCA on scaled data characteristics for all simulation pipelines.S_Fig_4: Heatmaps for proportion of significant features for each DA test and template in different color scales.S_Fig_5: Decision trees for all hypotheses.S_Text_1: Statistical Hypotheses for aim 2a.S_Text_2: Statistical Hypotheses for aim 2b.S_Text_3: Summary information for experimental templates.S_Text_4: Source code indicating how the DA methods were called, including configuration parametersS_Table_1.xlsx: Number of significant taxa for unfiltered data minus number of significant taxa for filtered dataS_Table_2.xlsx: Estimated coefficients of stepwise logistic regression for the identified predictive DCs<br>