Bayesian analysis of human microbiome compositional data. Modelling and prediction of the changes in its composition

• Autores: Irene Creus Martí
• Directores de la Tesis: Andrés Moya Simarro (dir. tes.) , Francisco José Santonja Gómez (codir. tes.)
• Lectura: En la Universitat de València ( España ) en 2023
• Idioma: inglés
• Número de páginas: 194
• Tribunal Calificador de la Tesis: Enriqueta Vercher González (presid.) , Josep Antoni Martín Fernández (secret.) , Eugeni Belda Cuesta (voc.)
• Enlaces
  • Tesis en acceso abierto en: RODERIC
• Resumen

  • Gut microbiome is related with the health status of subjects and recent studies highlight the importance of studying longitudinal microbiome data to analyse microbiome dynamics. However, it is known that microbiome data are high-dimensional and compositional which leads to strong statistical and computational challenges. In this thesis, we develop two models whose objective is to analyse microbiome time series in order to extract information about bacterial behaviour. These models are not focus on pair-wise interaction and take into account interactions between groups of bacterial taxa using balances (Egozcue and Pawlowsky-Glahn, 2005). Balances are a compositional tool useful to extract information about the relationship between the group of bacteria present at the numerator and the group of bacterial present at the denominator of the balance. Both models consider that the relative abundance of the bacterial taxa at each time point follows a Dirichlet distribution.

    In the Frequency Balances Model (FBM) (Creus-Martí et al., 2021) we model relative abundances of microbial taxa with a Dirichlet distribution with time-varying parameters. We assume that these relative abundances, after a log-ratio transformation, can be explained by an autoregressive structure which takes into account the effect of the bacterial community as a whole. This proposal can be useful to understand the relationships between microbes and the identification of keystone members of the microbial ecosystem that may play an important role.

    The Bayesian Principal Balances Model (BPBM) (Creus-Martí et al., 2022) is based on modeling a normalized transformation of the observed counts. We assume that normalized transformation of the observed counts can be explained by an autoregressive structure considering Dirichlet distribution with time-varying parameters which takes into account principal balances.

    Finally, we use these models to extract relevant biological information about the bacterial dynamics when there is antibiotic intake. We found that there is evidence of the evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment (Creus-Martí et al., 2023).

    I. Creus-Martí, J. Marín-Miret A. Moya, F.J. Santonja (2023). Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment. Mathematical Biosciences.

    https://doi.org/10.1016/j.mbs.2023.109057 I. Creus-Martí, A. Moya, F.J. Santonja (2022). Bayesian Hierarchical Compositional Models for Analysing Longitudinal Abundance Data from Microbiome Studies. Complexity. https: //doi.org/10.1155/2022/4907527 I. Creus-Martí, A. Moya, F.J. Santonja (2021). A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data. Complexity. https://doi.org/10.1155/2021/9951817 Egozcue, J. J. and Pawlowsky-Glahn, V. (2005). Groups of parts and their balances in compositional data analysis. Mathematical Geology, 37:795¿828.