BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:&quot\;A Bayesian analysis of microbiome data&quot\; - Dr Sergio B
 acallado\, University of Cambridge
DTSTART:20161129T143000Z
DTEND:20161129T153000Z
UID:TALK67164@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Techniques based on sequencing 16S ribosomal DNA have been use
 d for several years to characterise microbial communities. We introduce a 
 Bayesian nonparametric analysis of dependent discrete distributions which 
 can be applied to the analysis of these experiments. The procedure deals e
 ffectively with rare species without the need for truncation or rarefactio
 n\, and the model makes it possible to infer species co-occurrence pattern
 s that are usually observed in these datasets\, unlike other Bayesian appr
 oaches such as the Dirichlet-Multinomial model without prior dependence. T
 he dependence between distributions is expressed by latent features\, whic
 h makes the model especially suited to the joint analysis of multi-omic ex
 periments which might generate\, for example\, metabolic profiles\, RNA ex
 pression data\, or proteomics data in addition to the species-level charac
 terisation of a 16S sequencing experiment. We describe a simple approach t
 o visualise the posterior uncertainty in ecological ordinations typically 
 applied in microbiome studies.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
END:VEVENT
END:VCALENDAR