Network analysis is a fundamental tool for elucidating microbial interactions, which are crucial for understanding the mechanisms that shape ecosystem structure and function. However, aggregated co-abundance/co-occurrence network approaches that infer pairwise relationships among biological entities from large sample collections often overlook sample-specific interaction patterns. To address this limitation, we developed MicroSSNet, an R package designed for analyzing microbial networks, including both aggregated and single-sample networks. We designed MicroSSNet primarily to fill the current gap in bioinformatics tools for constructing single-sample networks (SSNs) from microbiome data, and we evaluated both the performance and limitations of ssPCC-based SSNs using simulated and real datasets. Through Monte Carlo simulations, we assessed the statistical behavior of ssPCC and highlighted scenarios in which ssPCC is less powerful. We then applied MicroSSNet to two distinct datasets: a human gut metagenomic dataset and a soil 16S rRNA gene dataset. In the human gut dataset, SSNs revealed unique edges not detected in the aggregated network. In the soil dataset, SSN features showed some predictive value for group classification. However, SSN-derived patterns should be interpreted cautiously, as they may not exclusively reflect true interaction changes. MicroSSNet additionally implements a full aggregated-network workflow, including bipartite networks and extensive topological property analysis. Together, MicroSSNet offers a framework for constructing and analyzing both single-sample and aggregated microbial networks. In this work, we also highlight the potential and limitations of single-sample network approaches, supporting their application as exploratory tools in microbiome research across individual and population levels. The package is freely available on GitHub (https://github.com/TangZecheng622/MicroSSNet).
Tang et al. (Mon,) studied this question.