Fungal Network Analysis

Software to extract and analyse fungal networks.

• FungalNetworkAnalysis.mlappinstall - installs the software as a matlab app (requires Matlab 2020b or later). 
• FungalNetworkAnalysis.exe - installs a standalone package for Windows 10. This will automatically download the Matlab runtime library from the web during installation. .
• FungalNetworkAnalysis_Manual.pdf - A user manual describing installation and use of the software.

Images of fungal mycelia can be loaded into the FungalNetworkAnalysis GUI and processed through a standard pipeline that includes noise reduction, background correction, network enhancement, segmentation, skeletonization, width estimation and network graph representation.

As fungal mycelia span a range of scales, with differing contrast and noise levels depending on the imaging method, a number of different curvilinear feature enhancement methods and skeletonisation algorithms are provided to extract the network structure as a single-pixel wide binary skeleton. One of the most critical features of network analysis is to ensure that parts of the network do not become accidentally disconnected during extraction, as this may have a major impact on prediction of functional flows on the network and robustness measurements. Thus, the approach used here initially over-segments the network to ensure connectivity, calculates an initial graph and then prunes the individual edges using a combination of metrics. The pruned skeleton is then re-converted to a fully weighted graph representation, with nodes at the junctions or branch points, linked by edges with a vector of properties such as width, length and orientation. Once in a graph format, a wide range of graph theoretic measures can be calculated.

Please cite:

Aguilar-Trigueros, C.A., Boddy, L., Rillig, M.C. and Fricker, M.D. (2022) Network traits predict ecological strategies in fungi. ISME Communications. 2, 2

https://doi.org/10.1038/s43705-021-00085-1

The original images and parameter files from the paper can also be downloaded from:

https://doi.org/10.5281/zenodo.5725750