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 in 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., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling 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.
Highlights
Microbial communities are highly dynamic biological systems that cannot be fully investigated in snapshot studies
Interpolation of Missing Time Points We evaluated the ability of linear mixed model spline (LMMS) to interpolate an increasing number of missing time points
Interpolation is important in our framework as it allows the estimation of evenly spaced time points as well as time points that may be missing in one data set but not in the other
Summary
Microbial communities are highly dynamic biological systems that cannot be fully investigated in snapshot studies. Methods including loess (Shields-Cutler et al, 2018), smoothing spline ANOVA (Paulson et al, 2017), negative binomial smoothing splines (Metwally et al, 2018), or Gaussian cubic splines (Luo et al, 2017) were proposed to model dynamics of microbial profiles across groups of samples or subjects. The aim of these approaches is to make statistical inferences about global changes of differential abundance across multiple phenotypes of interest, rather than at specific time points. Baksi et al (2018) used a Jenson– Shannon divergence metric to visually compare metagenomic time series
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