Online Program Home
  My Program

Abstract Details

Activity Number: 221 - Advanced Statistical Methods for Microbiome Data Analysis
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #324737 View Presentation
Title: Microbiome Data Classification Under Sampling Zeros
Author(s): Zachary Kurtz* and Christian Müller and Richard Bonneau
Companies: and Flat Iron Institute, Simons Foundation and Flat Iron Institute, Simons Foundation
Keywords: microbiome ; computational biology ; compositional data analysis ; metric learning
Abstract:

We propose a novel method for distance metric learning for multi-class compositional data. This problem setup is motivated by common learning tasks on data sets arising in microbial ecology, such as relative abundances generated from 16S sequencing experiments. Our approach can specifically handle data that contain a large number of zero measurements (zero inflation), a common property for data acquired from targeted, high throughput sequencing. In previous work, Generalized Aitchison Embeddings were proposed as an extension of John Aitchison's log-ratio based framework to map image histograms from the Simplex to a suitable Euclidean space. We propose a novel algorithm to learn a Mahalanobis metric from microbial compositions given some meta-data (i.e. sample class), posed as a non-convex optimization problem.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association