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Belz Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>High-throughput sequencing data lie at the heart of modern microbiome research. Effective analysis of these data requires careful preprocessing, modeling, and interpretation to detect subtle signals and avoid spurious associations. In this review, we discuss how simulation can serve as a sandbox to test candidate approaches, creating a setting that mimics real data while providing ground truth. This is particularly valuable for power analysis, methods benchmarking, and reliability analysis. We explain the probability, multivariate analysis, and regression concepts behind modern simulators and how different implementations make trade-offs between generality, faithfulness, and controllability. Recognizing that all simulators only approximate reality, we review methods to evaluate how accurately they reflect key properties. We also present case studies demonstrating the value of simulation in differential abundance testing, dimensionality reduction, network analysis, and data integration. Code for these examples is available in an online tutorial (https:\/\/go.wisc.edu\/8994yz) that can be easily adapted to new problem settings.<\/jats:p>","DOI":"10.1093\/bib\/bbaf051","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T08:27:47Z","timestamp":1739176067000},"source":"Crossref","is-referenced-by-count":7,"title":["Semisynthetic simulation for microbiome data analysis"],"prefix":"10.1093","volume":"26","author":[{"given":"Kris","family":"Sankaran","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Wisconsin-Madison , 1300 University Ave, Madison,WI 53703 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saritha","family":"Kodikara","sequence":"additional","affiliation":[{"name":"Melbourne Integrative Genomics , School of Mathematics and Statistics, , Building 184\/30 Royal Parade, Melbourne, VIC 3052 ,","place":["Australia"]},{"name":"University of Melbourne , School of Mathematics and Statistics, , Building 184\/30 Royal Parade, Melbourne, VIC 3052 ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi Jessica","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, University of California, Los Angeles , 520 Portola Plaza, Los Angeles, CA 90095 ,","place":["United States"]},{"name":"Department of Human Genetics, University of California, Los Angeles , 695 Charles E Young Dr S, Los Angeles, CA 90095 ,","place":["United States"]},{"name":"Department of Biostatistics, University of California, Los Angeles , 650 Charles E. Young Dr S, Los Angeles, CA 90095 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim-Anh L\u00ea","family":"Cao","sequence":"additional","affiliation":[{"name":"Melbourne Integrative Genomics , School of Mathematics and Statistics, , Building 184\/30 Royal Parade, Melbourne, VIC 3052 ,","place":["Australia"]},{"name":"University of Melbourne , School of Mathematics and Statistics, , Building 184\/30 Royal Parade, Melbourne, VIC 3052 ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"2025021013273725400_ref1","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1128\/MMBR.68.4.669-685.2004","article-title":"Metagenomics: application of genomics to uncultured microorganisms","volume":"68","author":"Handelsman","year":"2004","journal-title":"Microbiol Mol Biol 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