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SUMMARY:A Bayesian ordination method for 16S microbiome profiling data - D
 r Sergio Bacallado
DTSTART:20160822T150000Z
DTEND:20160822T160000Z
UID:TALK66791@talks.cam.ac.uk
CONTACT:46487
DESCRIPTION:Abstract: We develop a statistical model to analyse microbiome
  profiling data based on sequencing of genetic fingerprints in 16S ribosom
 al RNA. The analysis allows us to quantify the uncertainty in ecological o
 rdination and clustering methods commonly applied in microbiome research. 
 The method is based on the estimation of the underlying microbial distribu
 tion in experimental samples using a dependent Dirichet Process prior in w
 hich dependence is expressed through low-dimensional latent features. This
  type of model is advantageous for several reasons. First\, information is
  borrowed across samples to estimate underlying microbial distributions. S
 econd\, the nonparametric nature of the model avoids the artefacts of trun
 cation and rarefaction techniques. Lastly\, the Bayesian framework mitigat
 es the effects of multiple testing for differential abundance and other hy
 potheses of interest. \n\nBio: Sergio Bacallado is a lecturer in the Stati
 stical Laboratory\, in the Dept. of Pure Mathematics and Mathematical Stat
 istics at the University of Cambridge. He completed a PhD in Structural Bi
 ology at the Stanford Medical School\, followed by a research fellowship i
 n the Department of Statistics at Stanford. His research interests include
  the analysis of dynamical simulations\, such as Molecular Dynamics\, and 
 applications of Bayesian methods to infer biological mechanisms from high-
 dimensional\, heterogeneous data.
LOCATION:CRUK CI\, Room 009/009A
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