Translator Disclaimer
June 2019 Adaptive gPCA: A method for structured dimensionality reduction with applications to microbiome data
Julia Fukuyama
Ann. Appl. Stat. 13(2): 1043-1067 (June 2019). DOI: 10.1214/18-AOAS1227

Abstract

Exploratory analysis is an important first step for discovering latent structure and generating hypotheses in large biological data sets. However, when the number of variables is large compared to the number of samples, standard methods such as principal components analysis give results that are unstable and difficult to interpret.

Here, we present adaptive generalized principal components analysis (adaptive gPCA), a new method that solves these problems by incorporating information about the relationships among the variables. Adaptive gPCA gives a low-dimensional representation of the samples with axes that are interpretable in terms of groups of closely related variables. We show that adaptive gPCA does well at reconstructing true latent structure in simulated data and demonstrate its use on a study of the effect of antibiotics on the human gut microbiota.

Citation

Download Citation

Julia Fukuyama. "Adaptive gPCA: A method for structured dimensionality reduction with applications to microbiome data." Ann. Appl. Stat. 13 (2) 1043 - 1067, June 2019. https://doi.org/10.1214/18-AOAS1227

Information

Received: 1 April 2018; Revised: 1 October 2018; Published: June 2019
First available in Project Euclid: 17 June 2019

zbMATH: 1423.62143
MathSciNet: MR3963562
Digital Object Identifier: 10.1214/18-AOAS1227

Rights: Copyright © 2019 Institute of Mathematical Statistics

JOURNAL ARTICLE
25 PAGES

This article is only available to subscribers.
It is not available for individual sale.
+ SAVE TO MY LIBRARY

SHARE
Vol.13 • No. 2 • June 2019
Back to Top