The human microbiome is a complex assembly of bacteria that are sensitive to many perturbations. We have developed specific tools for studying the vaginal, intestinal and oral microbiomes under different perturbations (pregnancy, hypo-salivation inducing medications and antibiotics are some examples).
A suite of statistical tools written in Bioconductor packages (phyloseq and dada2) allows for easy denoising,normalization, visualization and statistical testing of the longitudinal multi-table data composed of 16s rRNA reads combined with clinical data, transcriptomic and metabolomic profiles. Challenges we have had to address include information leaks, the integration of phylogenetic information,
longitudinal dependencies and uncertainty quantification.
The multiplicity of normalization and modeling choices during the analyses
make inference on these data particularly difficult.
This contains joint work with Joey McMurdie, Sergio Bacallado, Ben Callahan, Julia Fukuyama, Kris Sankaran, and David Relman’s Lab members from Stanford.
- Speaker: Susan Holmes (Stanford)
- Monday 08 May 2017, 14:00–15:00
- Venue: MR11, Centre for Mathematical Sciences, Wilberforce Road, Cambridge.
- Series: Statistics; organiser: Quentin Berthet.