Online Program Home
My Program

Abstract Details

Activity Number: 119
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: International Indian Statistical Association
Abstract #319072
Title: Microbiome Normalization Methods: Effect on Ordination Analysis
Author(s): Ekaterina Smirnova* and Snehalata Huzurbazar and Glen Alan Satten and Liyang Diao
Companies: University of Wyoming and University of Wyoming and CDC and Yale University
Keywords: microbiome ; small-n large-p ; data normalization ; RNA-seq ; ordination

Microbiome next generation sequencing experiments measure counts of DNA fragments for a large number of species in a sample. The total number of reads might vary dramatically across samples, and therefore appropriate scaling is necessary for any analysis. Prior to data analysis, normalization and bias adjustment are often implemented using filtering, library size adjustment and variance stabilization methods. Methods developed for RNA-seq data are being used for microbiome data, however the latter are extremely sparse and often dominated by a small number of species. Using methods from RNA-seq literature, and recently developed methods for adjustment of microbiome data, we investigate the effects of normalization and bias adjustment on ordination based analyses.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association