This workflow is meant to illustrate an approach for the inference and analysis of microbial association networks from cheese microbiota data extracted from DairyFMBN, a repository of data on the composition of bacterial communities in dairy products.
The workflow makes use of the NetCoMi package, using phyloseq objects extracted from DairyFMBN (only 5 are used here, but the workflow has been tested with 35 datasets and will run smoothly even with a computer with 8 GB RAM). The workflow has also been tested on phyloseq objects obtained from raw sequence data (see for example SRP212264) analyzed using the DADA2 pipeline (with SILVA as a taxonomic database).
To test the workflow, clone the folder, create your own project in RStudio using the Worklow folder, then open the MAN_NetCoMiFMBN_genus_example.Rmd file and run all chunks. A rendered .html is provided for convenience but the working environment has been cleared.
The workflow makes use of a number of user defined functions, which can be found in the subfolder source in folder workflow. Note that a lookup table is used to (optionally) convert the taxon names of the genus Lactobacillus to the new classification described by Zheng et al. (2020).
If you want to try it on your own data:
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put the phyloseq objects in the input_data folder (you should use .rds files)
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remove all content from the netcompare_output folder (here it is were all output files will be saved)
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set the options in chunks 1 and 2
The workflow has been tested on two computers with 8 GB RAM running MacOS 10.14.6 with R 4.0.5 and 4.1.
Comments and suggestions are welcome.
Known issues
The epiR::epi.2by2() changed recently (June 1st, 2021). If the odds_ratio() function does not work, this is the likely culprit. The function will therefore throw an error if epiR version is <2.0.26 but I cannot guarantee what happens in the future. There is also apparently a problem with the netAnalyze() function of version 1.0.2 of NetCoMi which calculates statistics for the Largest Connected Component in such a way that for very sparse and disconnected networks wrong values for some centralities may be obtained depending on which component is chosen as LCC. This is difficult to anticipate, so make sure to check results for consistency.