Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity
Abstract The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across s...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-57670-2 |
| _version_ | 1797233575293091840 |
|---|---|
| author | Beibei Wang Fengzhu Sun Yihui Luan |
| author_facet | Beibei Wang Fengzhu Sun Yihui Luan |
| author_sort | Beibei Wang |
| collection | DOAJ |
| description | Abstract The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis. |
| first_indexed | 2024-04-24T16:18:21Z |
| format | Article |
| id | doaj.art-18a0be5393cb4320a19c650232edd13b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T16:18:21Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-18a0be5393cb4320a19c650232edd13b2024-03-31T11:20:00ZengNature PortfolioScientific Reports2045-23222024-03-0114111610.1038/s41598-024-57670-2Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneityBeibei Wang0Fengzhu Sun1Yihui Luan2Frontier Science Center for Nonlinear Expectations, Ministry of EducationQuantitative and Computational Biology Department, University of Southern CaliforniaFrontier Science Center for Nonlinear Expectations, Ministry of EducationAbstract The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.https://doi.org/10.1038/s41598-024-57670-2 |
| spellingShingle | Beibei Wang Fengzhu Sun Yihui Luan Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity Scientific Reports |
| title | Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity |
| title_full | Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity |
| title_fullStr | Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity |
| title_full_unstemmed | Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity |
| title_short | Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity |
| title_sort | comparison of the effectiveness of different normalization methods for metagenomic cross study phenotype prediction under heterogeneity |
| url | https://doi.org/10.1038/s41598-024-57670-2 |
| work_keys_str_mv | AT beibeiwang comparisonoftheeffectivenessofdifferentnormalizationmethodsformetagenomiccrossstudyphenotypepredictionunderheterogeneity AT fengzhusun comparisonoftheeffectivenessofdifferentnormalizationmethodsformetagenomiccrossstudyphenotypepredictionunderheterogeneity AT yihuiluan comparisonoftheeffectivenessofdifferentnormalizationmethodsformetagenomiccrossstudyphenotypepredictionunderheterogeneity |