Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, 266237, China.
Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
Sci Rep. 2024 Mar 25;14(1):7024. doi: 10.1038/s41598-024-57670-2.
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.
人类微生物组由存在于人体内部和表面的微生物组成，在各种生理过程中发挥着关键作用，并与许多疾病有关。为了分析微生物组数据，必须考虑到样本之间固有的异质性和可变性。已经提出了一些标准化方法来减轻这些变化并提高可比性。然而，这些方法在预测二元表型方面的性能仍有待研究。本研究系统地评估了微生物组数据分析中的不同标准化方法及其对疾病预测的影响。我们的研究结果突出了缩放、成分数据分析、转换和批次校正方法的优缺点。缩放方法（如 TMM）表现出一致的性能，而成分数据分析方法则表现出混合的结果。转换方法（如 Blom 和 NPN）在捕捉复杂关联方面表现出了一定的潜力。批次校正方法（包括 BMC 和 Limma）始终优于其他方法。然而，标准化方法的影响受到人群效应、疾病效应和批次效应的限制。这些结果为在微生物组研究中选择合适的标准化方法、改进预测模型和推进个性化医学提供了参考。未来的研究应探索更大、更多样化的数据集，并为微生物组数据分析开发定制化的标准化策略。
