Comparison of zero replacement strategies for compositional data with large numbers of zeros

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.

Automated summary

Modern applications in chemometrics and bioinformatics often involve compositional data sets with a high proportion of zeros, such as microbiome data. When building statistical models, it is crucial to replace zeros with sensible values. Different replacement techniques are compared, including a method based on deep learning, to provide insights into their appropriateness for specific problems and discuss differences in statistical results.