Work

Differential Network Analysis for Compositional Data

Year: 2026

Type: article

Abstract: It is often of interest to understand how conditional dependence relationships among components 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.

Source: Statistica Sinica

DOI: 10.5705/ss.202026.0149

Authors: Chencheng Ma, Hao Chen, Yijuan Hu, Xiang Zhan

Institutions: Shandong University, Southeast University, Southeast University, Peking University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W7225326329

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