Abstract

It is often of interest to understand how conditional dependence relationships among com

ponents of a compositional vector differs between two conditions. In this paper, we specify such a

conditional dependence network among components using a compositional precision matrix. To perform differential network analysis for compositional data, we propose DNA-Coda that directly estimates

the difference of two compositional precision matrices. Under mild regularity conditions, our approach

is shown to be consistent in support recovery and estimation. Comprehensive simulations demonstrate

DNA-Coda’s statistical superiority over existing methods in the accuracy of estimation and recovery

of true differential network matrix. The potential usefulness of our method is also illustrated by an

application example to differential network analysis of microbiome compositional data.

Key words and phrases: Compositional data analysis (Coda), Differential network analysis, High dimensionality, Precision matrix 1

Information

Preprint No.SS-2026-0149
Manuscript IDSS-2026-0149
Complete AuthorsChencheng Ma, Hao Chen, Yijuan Hu, Xiang Zhan
Corresponding AuthorsXiang Zhan
Emailszhanx@seu.edu.cn

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Supplementary Materials

The Supplementary Material contains implementation details of DNA-Coda and technical

proofs of theorems.

Supplementary materials are available for download.