gms | German Medical Science

Background: Network analysis methods are suitable for investigating the microbial interplay within a habitat. Since microbial associations may change between conditions, e.g. between health and disease state, comparing microbial association networks between two groups might be useful. Two approaches for pinpointing differences are: 1) differential association analysis, which tests for differences in the associations themselves, and 2) differential network analysis, which focuses on differences in network topologies of two separately constructed networks.

Estimating associations for sequencing data is challenging due to their special characteristics – that is, sparsity with a high number of zeros, high dimensionality, and compositionality. Several association measures taking these features into account have been published during the last decade. Furthermore, several network analysis tools, methods for comparing network properties among two or more groups as well as approaches for constructing differential networks are available in the literature. However, no unifying tool for the whole process of constructing, analyzing and comparing microbial association networks between groups is available so far.

Methods: We provide a new R package NetCoMi (Network Construction and Comparison for Microbiome Data) implementing this whole workflow starting with a read count matrix originating from a sequencing process, to network construction, up to a statement whether local network characteristics, the determined clusters, or even the overall network structure differs between two groups. For each of the aforementioned steps, a selection of existing methods suitable for the application on microbiome data is included. Especially the function for network construction contains many different approaches including methods for treating zeros in the data, normalization, computing microbial associations, and sparsifying the resulting association matrix. NetCoMi can either be used for constructing, analyzing and visualizing a single network, or for comparing two networks in a graphical as well as a quantitative manner, including statistical tests. Our package furthermore offers a function for constructing a differential network, where only differentially associated organisms are connected.

Results: We illustrate the application of our package using a real data set from GABRIELA study [1] to compare microbial associations in settled dust from children's rooms between samples from two study centers. The examples demonstrate how our proposed graphical methods uncover genera with different characteristics (e.g. a different centrality) between the groups, similarities and differences between the clusterings, as well as differences among the associations themselves. These descriptive findings are confirmed by a quantitative output including a statement whether the results are statistically significant.

Conclusion: With NetCoMi we offer researchers the possibility for constructing, analyzing and comparing microbial association networks in only a few steps while being able to choose from existing – common as well as novel – approaches suitable for the application on sequencing data. Thus, our package facilitates the analysis of real data sets on the one hand and is suitable for simulation studies evaluating and comparing the performance of the implemented approaches on the other hand.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.