llperm: a permutation of regressor residuals test for microbiome data
Abstract Background Differential abundance testing is an important aspect of microbiome data analysis, where each taxa is fitted with a statistical test or a regression model. However, many models do not provide a good fit to real microbiome data. This has been shown to result in high false positive...
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05088-w |
| _version_ | 1811291406784790528 |
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| author | Markus Viljanen Hendriek Boshuizen |
| author_facet | Markus Viljanen Hendriek Boshuizen |
| author_sort | Markus Viljanen |
| collection | DOAJ |
| description | Abstract Background Differential abundance testing is an important aspect of microbiome data analysis, where each taxa is fitted with a statistical test or a regression model. However, many models do not provide a good fit to real microbiome data. This has been shown to result in high false positive rates. Permutation tests are a good alternative, but a regression approach is desired for small data sets with many covariates, where stratification is not an option. Results We implement an R package ‘llperm’ where the The Permutation of Regressor Residuals (PRR) test can be applied to any likelihood based model, not only generalized linear models. This enables distributions with zero-inflation and overdispersion, making the test suitable for count regression models popular in microbiome data analysis. Simulations based on a real data set show that the PRR-test approach is able to maintain the correct nominal false positive rate expected from the null hypothesis, while having equal or greater power to detect the true positives as models based on likelihood at a given false positive rate. Conclusions Standard count regression models can have a shockingly high false positive rate in microbiome data sets. As they may lead to false conclusions, the guaranteed nominal false positive rate gained from the PRR-test can be viewed as a major benefit. |
| first_indexed | 2024-04-13T04:28:55Z |
| format | Article |
| id | doaj.art-c20ff2bd6b4742d296d9b3c30ce1780d |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T04:28:55Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c20ff2bd6b4742d296d9b3c30ce1780d2022-12-22T03:02:24ZengBMCBMC Bioinformatics1471-21052022-12-0123111910.1186/s12859-022-05088-wllperm: a permutation of regressor residuals test for microbiome dataMarkus Viljanen0Hendriek Boshuizen1National Institute for Public Health and the Environment - RIVMNational Institute for Public Health and the Environment - RIVMAbstract Background Differential abundance testing is an important aspect of microbiome data analysis, where each taxa is fitted with a statistical test or a regression model. However, many models do not provide a good fit to real microbiome data. This has been shown to result in high false positive rates. Permutation tests are a good alternative, but a regression approach is desired for small data sets with many covariates, where stratification is not an option. Results We implement an R package ‘llperm’ where the The Permutation of Regressor Residuals (PRR) test can be applied to any likelihood based model, not only generalized linear models. This enables distributions with zero-inflation and overdispersion, making the test suitable for count regression models popular in microbiome data analysis. Simulations based on a real data set show that the PRR-test approach is able to maintain the correct nominal false positive rate expected from the null hypothesis, while having equal or greater power to detect the true positives as models based on likelihood at a given false positive rate. Conclusions Standard count regression models can have a shockingly high false positive rate in microbiome data sets. As they may lead to false conclusions, the guaranteed nominal false positive rate gained from the PRR-test can be viewed as a major benefit.https://doi.org/10.1186/s12859-022-05088-wMicrobiomeBioinformaticsStatistics |
| spellingShingle | Markus Viljanen Hendriek Boshuizen llperm: a permutation of regressor residuals test for microbiome data BMC Bioinformatics Microbiome Bioinformatics Statistics |
| title | llperm: a permutation of regressor residuals test for microbiome data |
| title_full | llperm: a permutation of regressor residuals test for microbiome data |
| title_fullStr | llperm: a permutation of regressor residuals test for microbiome data |
| title_full_unstemmed | llperm: a permutation of regressor residuals test for microbiome data |
| title_short | llperm: a permutation of regressor residuals test for microbiome data |
| title_sort | llperm a permutation of regressor residuals test for microbiome data |
| topic | Microbiome Bioinformatics Statistics |
| url | https://doi.org/10.1186/s12859-022-05088-w |
| work_keys_str_mv | AT markusviljanen llpermapermutationofregressorresidualstestformicrobiomedata AT hendriekboshuizen llpermapermutationofregressorresidualstestformicrobiomedata |