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Direct interaction network and differential network inference from compositional data via lasso penalized D-trace

Shun He¹, Minghua Deng^1,2

¹School of Mathematical Sciences, Peking University, Beijing, 10087, P.R. China.

|July 25, 2019

Summary

This study introduces novel methods for analyzing complex microbial community data from 16S rRNA gene sequencing. The new approach improves the inference of microbial interaction networks and their changes across conditions.

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

High-throughput sequencing of 16S rRNA genes yields high-quality compositional data for microbial communities.
Inferring microbial interaction networks and their dynamic changes across conditions is crucial but challenging due to data characteristics.

Purpose of the Study:

To develop novel statistical methods for robust network and differential network inference from compositional microbiome data.
To address the challenges posed by the compositional nature and high dimensionality of 16S rRNA gene profiling data.

Main Methods:

Proposed two new loss functions integrating compositional data analysis transformations into D-trace loss.
Utilized lasso penalized loss and the ADMM algorithm for sparse matrix estimation and numerical solutions.
Developed methods for network and differential network estimation tailored for high-dimensional compositional data.

Main Results:

Simulations demonstrated superior performance of the proposed method compared to existing state-of-the-art techniques.
The method accurately infers microbial interaction networks and detects differential networks across various scenarios.
Successfully applied the method to analyze a mouse skin microbiome dataset, illustrating its practical utility.

Conclusions:

The developed methods offer a robust solution for microbial network inference from challenging compositional data.
This work advances the analysis of microbiome data, enabling deeper understanding of microbial community interactions.
The approach provides a valuable tool for biological studies investigating microbial ecology and host-microbe interactions.