A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

Abstract

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical or phenotypic data are still at their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g. sparsity, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial communities data, and select key temporal features with strong associations at the sample group level. The framework ranges from filtering and modelling, to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

Integrative Analyses to Investigate the Link between Microbial Activity and Metabolite Degradation during Anaerobic Digestion

“…Similar trends were observed at the other dates (see supplemental figure S4). The quality of the clustering was further assessed by comparing the proportionality distances between profiles (Bodein et al, 2019;Quinn et al, 2017). The distances were lower between profiles grouped within the same cluster, compared to the distances with the profiles assigned to other clusters (supplemental Figure S3 and Table S3).…”

Interpretation of network-based integration from multi-omics longitudinal data

“…Low counts are filtered and data are normalized according to the type of data in each table. We also applied a filter on time profiles and kept only molecules with the highest expression fold change between the lowest and highest point over the entire time course, as described in [3]. For each case study, we adapted these filters to take into account platform-specific dynamic range of values.…”

“…The timeOmics approach [3] was used to cluster multi-omics molecules with similar expression profiles over time. The framework is based on 2 main steps:…”

A Dirichlet Autoregressive Model for the Analysis of Microbiota Time‐Series Data

“…Recent works also propose the use of state-space models, which assume that abundances are associated with a real-value hidden state variable vector that evolves through time based on a firstorder Markov process and can identify the microbial interaction [23][24][25]. Other alternatives for the analysis of microbial community temporal dynamics are linear mixed models that provide flexibility in correlated longitudinal data [26,27] or dynamic Bayesian networks, which are another class of state-space models appropriate to model the interaction of microbial taxa [28].…”