# A novel robust network construction and analysis workflow for mining infant microbiota relationships

## TL;DR

The paper introduces a new workflow for analyzing infant gut microbiota relationships using a robust network construction method and identifies core microbial genera that change with age.

## Contribution

A novel probability-based co-detection model (PBCDM) and network shearing strategy for robust microbial network analysis in infant microbiota.

## Key findings

- The PBCDM method showed superior stability and robustness in network attributes compared to other methods.
- Core genera networks were identified, showing greater similarity between adjacent age ranges and increasing microbial competition with maturation.
- The workflow and PBCDM method could be applied to future studies of genomic, metabolic, and proteomic data.

## Abstract

The gut microbiota plays a crucial role in infant health, with its development during the first 1,000 days influencing health outcomes. Understanding the relationships within the microbiota is essential to linking its maturation process to these outcomes. Several network-based methods have been developed to analyze the developing patterns of infant microbiota, but evaluating the reliability and effectiveness of these approaches remains a challenge. In this study, we created a test data pool using public infant microbiome data sets to assess the performance of four different network-based methods, employing repeated sampling strategies. We found that our proposed Probability-Based Co-Detection Model (PBCDM) demonstrated the best stability and robustness, particularly in network attributes such as node counts, average links per node, and the positive-to-negative link (P/N) ratios. Using the PBCDM, we constructed microbial co-existence networks for infants at various ages, identifying core genera networks through a novel network shearing method. Analysis revealed that core genera were more similar between adjacent age ranges, with increasing competitive relationships among microbiota as the infant microbiome matured. In conclusion, the PBCDM-based networks reflect known features of infant microbiota and offer a promising approach for investigating microbial relationships. This methodology could also be applied to future studies of genomic, metabolic, and proteomic data.

As a research method and strategy, network analysis holds great potential for mining the relationships of bacteria. However, consistency and solid workflows to construct and evaluate the process of network analysis are lacking. Here, we provide a solid workflow to evaluate the performance of different microbial networks, and a novel probability-based co-existence network construction method used to decipher infant microbiota relationships. Besides, a network shearing strategy based on percolation theory is applied to find the core genera and connections in microbial networks at different age ranges. And the PBCDM method and the network shearing workflow hold potential for mining microbiota relationships, even possibly for the future deciphering of genome, metabolite, and protein data.