Next Meeting:

Pacific: FEBRUARY 8th 2022

Premier Session


🇺🇸  20.30pm New York (Day before)
🇬🇧  1.30am London
🇯🇵  10.30am Tokyo

Second Session


🇺🇸 10.00am New York
🇬🇧 15.00pm London
🇯🇵 00.00 am Tokyo (Day After)


Topic models for interpretable multidomain microbiome data

prof Susan Holmes


Data from sequencing bacterial communities are formalized as contingency tables whose columns correspond to different biological sample-specimens. The row-features are a random collection of Amplicon Sequence Variants (ASVs in the case of 16S rRNA type amplicon sequencing) or gene fragments (in the case of metagenomics). In both cases, these entities are defined after the data are collected, thus imposing a nonparametric framework. There are usually more features-rows than columns imposing necessary regularization through use of Bayesian priors.

However, the classical Dirichlet-multinomial models are insufficient to account for the strong associations (or exclusions) between certain bacteria, thus recent hierarchical models such as latent Dirichlet topic models have provided a more flexible framework that allow mixed membership models more appropriate for these non-Gaussian data.

We will show how these hierarchical topic models can enhance our understanding of both longitudinal dependencies between samples and biological dependencies between taxa, regardless of the differences in sampling depth and sources of variability.
This contains work with Kris Sankaran, Pratheepa Jeganathan, Laura Symul, Ben Callahan, and David Relman.


Trained in the French school of Data Analysis in Montpellier, Susan Holmes has been working in non-parametric multivariate statistics applied to Biology since 1985. She has taught at MIT, Harvard and was an Associate Professor of Biometry at Cornell before moving to Stanford in 1998. Her theoretical interests include applied probability, Monte Carlo Markov chains, Graph Limit Theory, Differential Geometry, and the topology of the space of Phylogenetic Trees. 

She uses computational statistics, in particular, nonparametric computer-intensive methods to draw inferences about many complex biological phenomena, mostly from the areas of Immunology, Cancer Biology, and Microbial Ecology. She is interested in integrating the information provided by phylogenetic trees, community interaction graphs, and metabolic networks with sequencing data and clinical covariates, mostly from the areas of Immunology, Cancer Biology, and Microbial Ecology. Her current focus is improving the statistical analyses and reproducibility of data in perturbation studies of the Human Microbiome.


Autism-related dietary preferences mediate autism-gut microbiome associations

Chloe X Yap

Mater Research Institute, University of Queensland; Institute for Molecular Bioscience, University of Queensland

See the abstract!

Using strain-resolved analysis to identify contamination in metagenomics data

Yue Clare Lou

University of California, Berkeley

See the abstract!

The genetic and ecological landscape of plasmids in the human gut

Michael Yu

Toyota Technological Institute at Chicago

See the abstract!



Identification of Gut Bacteria such as Lactobacillus johnsonii that Disseminate to Systemic Tissues of Wild Type and MyD88–/– Mice

Sreeram Udayan

Washington University School of Medicine in St Louis

See the abstract!

Processed foods drive intestinal barrier permeability and microvascular diseases

Matthew Snelson

Monash University

See the abstract!




Varun Aggarwala, Maria Carmen Collado, Chris Hoffman, Serena Manara, Eleonora Nigro, Nicola Segata, Levi Waldron

Hui Chong, Anna Cuscó, Lisa Karstens, Serena Manara, Felix Salim, Carra Simpson, Svetlana Ugarcina Perovic, Liu Yiting, Moreno Zolfo