Understanding both global and local patterns in the structure and interplay of microbial communities has been a fundamental question in ecological research. In this paper, we present a python toolbox that combines two emerging techniques that have been proposed as useful when analyzing compositional microbial data. On one hand, we introduce a visualization module that incorporates the use of UMAP, a recent dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density. On the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple this with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) using simulated community networks with known topologies to show that we are able to discern between network topologies. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data.
Stability Analysis of Biological Network Topologies during Stochastic Simulation