A critical comparison of handling zeros in high-dimensional compositional count data

2026/05/21 by Wenqi Tang, Kamila Fačevicová, Klaus Nordhausen +1 · 1 voice
Mathematics · #stat.OT

paper · pdf · doi:10.1016/j.chemolab.2026.105821

arxiv published 2026/05/21 · arxiv updated 2026/05/21 · crossref created 2026/07/29 · crossref deposited 2026/08/05 · crossref indexed 2026/08/05 · crossref issued 2026/10/01 · crossref published 2026/10/01 · crossref published-print 2026/10/01

Abstract

The growing use of high-throughput sequencing (HTS) has enabled the large-scale production of compositional count data, driving progress in microbiome research. However, such count data are often high-dimensional, over-dispersed, and heavily zero-inflated, and they conflict with the continuity assumptions underlying log-ratio-based compositional data analysis (CoDA), creating substantial methodological challenges. This review provides an overview of zero-handling strategies in compositional data, covering zero-tolerant transformations, imputation approaches for rounded zeros, and statistical models for essential zeros. We specifically highlight the problems that arise when applying the log-ratio framework to sequencing-derived compositional count data, where violations of continuity can induce numerical instabilities and biased statistical inferences. Motivated by these issues, we systematically examine how existing imputation strategies behave when adapted to discrete, zero-inflated count data, including an evaluation of how the discrete, lattice-valued nature of the data affects imputation performance. Overall, this review consolidates scattered methodological developments, clarifies appropriate use cases, and identifies open challenges that motivate future zero-handling frameworks capable of jointly accommodating compositional constraints, zero inflation, and the lattice nature of count data, while also providing a detailed discussion of the comparison results.

Citations

• Spatial Hyperspheric Models for Compositional Data
• A Bayesian Zero-Inflated Dirichlet-Multinomial Regression Model for Multivariate Compositional Count Data
• The chiPower transformation: a valid alternative to logratio transformations in compositional data analysis
• Modelling structural zeros in compositional data via a zero-censored multivariate normal model
• Artificial Neural Networks to Impute Rounded Zeros in Compositional Data
• A Bayesian Zero-Inflated Negative Binomial Regression Model for the Integrative Analysis of Microbiome Data
• Bayesian Nonparametric Ordination for the Analysis of Microbial Communities
• Improved classification for compositional data using the α-transformation
• A Dirichlet Regression Model for Compositional Data with Zeros
• Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
• mice: Multivariate Imputation by Chained Equations inR
• A data-based power transformation for compositional data
• MissForest - nonparametric missing value imputation for mixed-type data
• A Latent Gaussian Model for Compositional Data with Zeros
• The Statistical Analysis of Compositional Data.

Discussions

• Wenqi Tang, Kamila Fa\v{c}evicov\'a, Klaus Nordhausen, Sara Taskinen: A critical comparison of handling zeros in high-dimensional compositional count data https://arxiv.org/abs/2605.22181 https://arxi [bsky, 0 points, 0 comments]

Related

• Testing for differential abundance in compositional counts data, with application to microbiome studies
• Geometry-preserving and interpretable dimension reduction for compositional data
• A tree-based model for addressing sparsity and taxa covariance in microbiome compositional count data
• A New Approach to Compositional Data Analysis using \(L^∞\)-normalization with Applications to Vaginal Microbiome
• High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis