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Predictive analysis methods for human microbiome data with application to Parkinson’s disease

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  • Mei Dong
  • Longhai Li
  • Man Chen
  • Anthony Kusalik
  • Wei Xu

Abstract

Microbiome data consists of operational taxonomic unit (OTU) counts characterized by zero-inflation, over-dispersion, and grouping structure among samples. Currently, statistical testing methods are commonly performed to identify OTUs that are associated with a phenotype. The limitations of statistical testing methods include that the validity of p-values/q-values depend sensitively on the correctness of models and that the statistical significance does not necessarily imply predictivity. Predictive analysis using methods such as LASSO is an alternative approach for identifying associated OTUs and for measuring the predictability of the phenotype variable with OTUs and other covariate variables. We investigate three strategies of performing predictive analysis: (1) LASSO: fitting a LASSO multinomial logistic regression model to all OTU counts with specific transformation; (2) screening+GLM: screening OTUs with q-values returned by fitting a GLMM to each OTU, then fitting a GLM model using a subset of selected OTUs; (3) screening+LASSO: fitting a LASSO to a subset of OTUs selected with GLMM. We have conducted empirical studies using three simulation datasets generated using Dirichlet-multinomial models and a real gut microbiome data related to Parkinson’s disease to investigate the performance of the three strategies for predictive analysis. Our simulation studies show that the predictive performance of LASSO with appropriate variable transformation works remarkably well on zero-inflated data. Our results of real data analysis show that Parkinson’s disease can be predicted based on selected OTUs after the binary transformation, age, and sex with high accuracy (Error Rate = 0.199, AUC = 0.872, AUPRC = 0.912). These results provide strong evidences of the relationship between Parkinson’s disease and the gut microbiome.

Suggested Citation

  • Mei Dong & Longhai Li & Man Chen & Anthony Kusalik & Wei Xu, 2020. "Predictive analysis methods for human microbiome data with application to Parkinson’s disease," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
  • Handle: RePEc:plo:pone00:0237779
    DOI: 10.1371/journal.pone.0237779
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    References listed on IDEAS

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    1. Lizhen Xu & Andrew D Paterson & Williams Turpin & Wei Xu, 2015. "Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-30, July.
    2. Fan Xia & Jun Chen & Wing Kam Fung & Hongzhe Li, 2013. "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis," Biometrics, The International Biometric Society, vol. 69(4), pages 1053-1063, December.
    3. Tao Wang & Can Yang & Hongyu Zhao, 2019. "Prediction analysis for microbiome sequencing data," Biometrics, The International Biometric Society, vol. 75(3), pages 875-884, September.
    4. Tao Wang & Hongyu Zhao, 2017. "Constructing Predictive Microbial Signatures at Multiple Taxonomic Levels," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1022-1031, July.
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    Cited by:

    1. G. S. Monti & P. Filzmoser, 2022. "Robust logistic zero-sum regression for microbiome compositional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 301-324, June.

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