Comparison of zero replacement strategies for compositional data with large numbers of zeros
Date
2021-03
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
Modern applications in chemometrics and bioinformatics result in compositional data sets with a high proportion of zeros. An example are microbiome data, where zeros refer to measurements below the detection limit of one count. When building statistical models, it is important that zeros are replaced by sensible values. Different replacement techniques from compositional data analysis are considered and compared by a simulation study and examples. The comparison also includes a recently proposed method (Templ, 2020) [1] based on deep learning. Detailed insights into the appropriateness of the methods for a problem at hand are provided, and differences in the outcomes of statistical results are discussed.
Description
CITATION: Lubbe, S., Filzmoser, P. & Templ, M. 2021. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Elsevier Chemometrics and Intelligent Laboratory Systems 210(2021):11 pages. doi.10.1016/j.chemolab.2021.104248
The original publication is available at: sciencedirect.com
The original publication is available at: sciencedirect.com
Keywords
Statistical models, Regression analysis, Bioinformatics
Citation
Lubbe, S., Filzmoser, P. & Templ, M. 2021. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Elsevier Chemometrics and Intelligent Laboratory Systems 210(2021):11 pages.