Invited Talk (Breiman Lecture)

Reproducible Research: the Case of the Human Microbiome

Susan Holmes

2016 Invited Talk (Breiman Lecture)

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Abstract

Modern data sets usually present multiple levels of heterogeneity, some apparent such as the necessity of combining trees, graphs, contingency tables and continuous covariates, others concern latent factors and gradients. The biggest challenge in the analyses of these data comes from the necessity to maintain and percolate uncertainty throughout the analyses. I will present a completely reproducible workflow that combines the typical kernel multidimensional scaling approaches with Bayesian nonparametrics to arrive at visualizations that present honest projection regions.

This talk will include joint work with Kris Sankaran, Julia Fukuyama, Lan Huong Nguyen, Ben Callahan, Boyu Ren, Sergio Bacallado, Stefano Favaro, Lorenzo Trippa and the members of Dr Relman's research group at Stanford.

Speaker

Susan Holmes

Brought up in the French School of Data Analysis (Analyse des Données) in the 1980's, Professor Holmes specializes in exploring and visualizing complex biological data. She is interested in integrating the information provided by phylogenetic trees, community interaction graphs and metabolic networks with sequencing data and clinical covariates. She uses computational statistics, and Bayesian methods to draw inferences about many complex biological phenomena such as the human microbiome or the interactions between the immune system and cancer. She teaches using R and BioConductor and tries to make everything she does freely available.

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