The aim of r Rpackage("famat") is to allow users to determine functional links between metabolites and genes. These metabolites and genes lists may be related to a specific experiment/study, but r Rpackage("famat") only needs a gene symbols list and a Kegg Compound ids list. Using these lists, r Rpackage("famat") performs pathway enrichment analysis, direct interactions between elements inside pathways extraction, GO terms enrichment analysis, calculation of user's elements centrality (number of direct interactions between an element and others inside a pathway) and extraction of information related to user's elements.

Functions available are:


Run this command line to install r Rpackage("famat").

if (!requireNamespace("BiocManager", quietly = TRUE))


Then, load r Rpackage("famat") using library.



This function uses the metabolite list and the gene list provided by user to perform pathway enrichment analysis. Metabolites ids need to be Kegg compound ids, and genes ids need to be gene symbols. Three pathway databases are available: Kegg ("KEGG"), Wikipathways ("WP") and Reactome ("REAC").


listr=path_enrich("REAC", meta, genes)

Results are then stored into a list. This list must be used in "interactions" function. Pathways enrichment analysis is performed on genes using r CRANpkg("gprofiler2") and on metabolites using r CRANpkg("MPINet").


"Interactions" find all direct interactions between genes and metabolites of user's lists in pathways obtained through pathways enrichment analysis, performed on KEGG, Reactome and Wikipathways pathways. So, this function needs results of "path_enrich" function performed on all these databases. Using direct interactions, centrality of a user's element inside a pathway is calculated.


interactions_result = interactions(listk, listr, listw)

Results are then stored into a list. This list must be used in "compl_data" function. Direct interactions were collected from BioPax, KGML and GPML files parsed with r Biocpkg("PaxtoolsR"), r Biocpkg("graphite") and author's parsers. "Interactions" just get interactions of enriched pathways from this direct interactions list.


This function complete information about elements and pathway obtained with "path_enrich" and "interactions". A GO term enrichment analysis is performed on genes, pathways obtained through pathways enrichment analysis are filtered (they must contain at least 1/5 elements in user's lists or a direct interaction between user's elements) and a hierarchy parent-child is built with pathways and enriched GO terms. GO terms enrichment analysis is performed using r Biocpkg("clusterProfiler"). Then, dataframes containing information about elements, interactions and GO terms are created, with an heatmap showing which user's elements are in which pathways.


compl_data_result <- compl_data(interactions_result)

Results are then stored into a list. This list must be used in "rshiny" function.


All results obtained with the three previous functions can be visualized using "rshiny" function. r CRANpkg("shiny") is a R package allowing to create interfaces.



After using this command line, the r CRANpkg("shiny") interface appear.

test Interface's tabs are:

Finally, a "Reset" button was made to go back to the initial results.

Conclusion: how to use famat

To conclude, r Rpackage("famat") has four important functions which have to be used one after another:


listk <- path_enrich("KEGG", meta, genes)
listr <- path_enrich("REAC", meta, genes)
listw <- path_enrich("WP", meta, genes)

interactions_result <- interactions(listk, listr, listw)

compl_data_result <- compl_data(interactions_result)



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famat documentation built on Nov. 8, 2020, 5:56 p.m.