Deconvolving Taxonomic Contributions To Observed Differences In The Microbiome Of COPD Patient Groups | 9332
Journal of Computer Science & Systems Biology
Like us on:
Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.
While recent years have seen the development of tools for the analysis of microbiomic datasets generated using pyrosequencing
of the small 16S ribosomal subunit of bacteria, there do not exist rigorous statistical methods for determining the relative
contribution of various levels of taxonomic classification to observed differences in the microbiome of patients. Using existing
tools (e.g. the edgeR package in R) one can test for differences between the quantity of a given species between patient groups,
but there are no tools that allow a researcher to test for differences between species, genera, families, orders, classes and phyla
simultaneously in a rigorous manner. The simple approach of testing for a difference between 2 phyla by grouping all species within
those phyla into 2 groups and testing for a difference between the 2 groups is incorrect unless one accounts for measurements
coming from the same subject in some fashion. We have developed a method that allows one to conduct such an analysis by using
a negative binomial random effects model for the species level counts with random effects hierarchically grouped into the various
taxonomical levels. The method also allows for variance regularization. A Bayesian approach to inference is adopted and Markov
chain Monte Carlo is used to obtain parameter estimates and identify differences among the patient groups at all taxonomic
levels. We applied the method to a data set arising from pyrosequencing of bronchio-alveolar lavage fluid from 22 COPD patients
and 10 controls. The analysis identified differences at multiple taxonomic levels.
Cavan Reilly received his Ph.D from Columbia University in 2000 and became an Assistant Professor at the University of Minnesota the same year.
In 2007 he was promoted to Associate Professor at the same institution. He has published over 50 peer reviewed articles covering many topics at
the intersection of statistics, molecular biology and clinical medicine and has authored a textbook on this topic.
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals