Rigorous Statistical Methods for Rigorous Microbiome Science
ABSTRACT High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodivers...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2019-06-01
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| Series: | mSystems |
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| Online Access: | https://journals.asm.org/doi/10.1128/mSystems.00117-19 |
| _version_ | 1818742238050516992 |
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| author | Amy D. Willis |
| author_facet | Amy D. Willis |
| author_sort | Amy D. Willis |
| collection | DOAJ |
| description | ABSTRACT High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodiversity data. Developing statistical methods that produce valid P values requires thoughtful modeling and careful validation, but careful statistical analysis reduces the risk of false discoveries and increases scientific understanding. |
| first_indexed | 2024-12-18T02:09:20Z |
| format | Article |
| id | doaj.art-8a685d4d7a404b6d888906e960bb251d |
| institution | Directory Open Access Journal |
| issn | 2379-5077 |
| language | English |
| last_indexed | 2024-12-18T02:09:20Z |
| publishDate | 2019-06-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj.art-8a685d4d7a404b6d888906e960bb251d2022-12-21T21:24:31ZengAmerican Society for MicrobiologymSystems2379-50772019-06-014310.1128/mSystems.00117-19Rigorous Statistical Methods for Rigorous Microbiome ScienceAmy D. Willis0Department of Biostatistics, University of Washington, Seattle, Washington, USAABSTRACT High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodiversity data. Developing statistical methods that produce valid P values requires thoughtful modeling and careful validation, but careful statistical analysis reduces the risk of false discoveries and increases scientific understanding.https://journals.asm.org/doi/10.1128/mSystems.00117-19hypothesis testingmachine learningmodelingreproducibilitystatistics |
| spellingShingle | Amy D. Willis Rigorous Statistical Methods for Rigorous Microbiome Science mSystems hypothesis testing machine learning modeling reproducibility statistics |
| title | Rigorous Statistical Methods for Rigorous Microbiome Science |
| title_full | Rigorous Statistical Methods for Rigorous Microbiome Science |
| title_fullStr | Rigorous Statistical Methods for Rigorous Microbiome Science |
| title_full_unstemmed | Rigorous Statistical Methods for Rigorous Microbiome Science |
| title_short | Rigorous Statistical Methods for Rigorous Microbiome Science |
| title_sort | rigorous statistical methods for rigorous microbiome science |
| topic | hypothesis testing machine learning modeling reproducibility statistics |
| url | https://journals.asm.org/doi/10.1128/mSystems.00117-19 |
| work_keys_str_mv | AT amydwillis rigorousstatisticalmethodsforrigorousmicrobiomescience |