NetCoMi: network construction and comparison for microbiome data in R

Abstract

Motivation Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data. Results Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich). Availability R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi. Contact Tel:+49 89 3187 43258; stefanie.peschel@mail.de Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

Microbial association networks in cheese: a meta-analysis

“…to subsets of nodes and edges, and to rearrange the graph using layout algorithms with the purpose of identifying modules or hubs. The inference of microbial associations (or of more complex associations in meta-omic data) requires specialized tools (Liu et al, 2020;Peschel et al, 2020;Röttjers and Faust, 2018). Unfortunately, interactions between taxa, or between taxa and metabolites, are still being inferred from correlation (either Pearsons's r or rank order correlations) matrices using some sort of arbitrary threshold for sparsification (i.e.…”

“…The interested readers are encouraged to peruse one of the many recent reviews on the subject (Jiang et al, 2019;Liu et al, 2020;Röttjers and Faust, 2018). A few approaches have been used more frequently or tested in comparative studies and are available in NetCoMi, a R package specialized in microbial association network inference and network comparisons (Peschel et al, 2020). SparCC (Sparse Correlations for Compositional data; Friedman and Alm, 2012) infers networks based on Pearson correlations on log-ratio transformed data, thus addressing, at least in part, the issue of compositionality, and has been frequently used.…”

“…This has been reviewed multiple times (Liu et al, 2020;Röttjers and Faust, 2018;Weiss et al, 2016) and further discussion is beyond the scope of this paper. However, the availability of tools which allow rapid inference of networks with multiple methods (such as NetCoMi; Peschel et al, 2020) makes this task relatively easy (an example workflow is available from GitHub: https://github.com/ep142/MAN_in_cheese). We therefore carried out network inference on 34 cheese datasets included in DairyFMBN (see Supplementary Table 1) using 2 correlation based methods (SparCC and CCREPE) and two conditional dependence methods (SPRING and SPIEC-EASI) (see section 1.2).…”

“…Microbial association networks inferred from studies ST49 (Kamimura et al, 2019) and ST136 (Dolci et al, 2020) extracted from DairyFMBN 2.1.6, with no taxonomic aggregation (https://data.mendeley.com/datasets/3cwf729p34/5) using methods CCREPE (Faust et al, 2012) or SPIEC-EASI (Kurtz et al, 2015). For each pane, the color of nodes is determined by phylum, and the size by degree; the color of the edges is red for mutual exclusion relationships and green for copresence relationships; the thickness of the edges is determined by the strength of the association measure, as determined in NetCoMi (Peschel et al, 2020). A force-based layout (Fruchterman-Reingold) was used for positioning nodes and edges.…”

Effect of Differences in the Microbiome of Cyp17a1-Deficient Mice on Atherosclerotic Background

“…Correlation network inference on clr-normalized abundances was performed using the SparCC [ 45 ] approach, as implemented in the R package NetCoMi (v1.0.2) [ 46 ], and significant edges were selected using Student’s t -test. Community structures were estimated using greedy optimization of modularity, hub node detection was performed using a threshold of 0.8, and quantitative assessment of the network was performed using a permutation approach (100,000 bootstraps) with an adaptive Benjamini–Hochberg correction to adjust p -values for multiple testing.…”