Abstract

Co-authors: Kris Sankaran (Stanford), Julia Fukuyama (Stanford), Lan Nguyen (Stanford), Diana Proctor (Stanford), David Relman (Stanford), Sergio Bacallado (Cambridge), Boyu Ren (Stanford), Pratheepa Jeganathan (Stanford)
 
The human microbiome is a complex assembly of bacteria that are sensitive to many perturbations. In several longitudinal analyses we study perturbations of bacterial community networks over time. For this, we have developed specific tools for modeling the vaginal, intestinal and oral microbiomes under these different perturbations (pregnancy, hypo-salivation inducing medications and antibiotics are some examples).
 
A suite of statistical tools written in R based on a Bioconductor package (phyloseq) allows for easy normalization, visualization and statistical testing of the longitudinal multi-table data composed of 16sRNA reads combined with clinical data, transcriptomic and metabolomic profiles. Challenges we have had to address include information leaks, the heterogeneity of the data, multiplicity of choices during the analyses and validation of results.
 
Each different body site requires a different modeling strategy as some sites form tight communities easily modeled with Stochastic Block Models whereas others show more diverse assemblies that require complex latent variable models.

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