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
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("famat")
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").
data(genes) data(meta) listr=path_enrich("REAC", meta, genes)
Results are then stored into a list. This list must be used in "interactions"
Pathways enrichment analysis is performed on genes using
r CRANpkg("gprofiler2") and on metabolites using
"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.
data(listk) data(listr) data(listw) interactions_result = interactions(listk, listr, listw)
Results are then stored into a list. This list must be used in "compl_data"
Direct interactions were collected from BioPax, KGML and GPML files parsed
r Biocpkg("graphite") and author's parsers.
"Interactions" just get interactions of enriched pathways from this direct
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
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.
data(interactions_result) 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
r CRANpkg("shiny") is a R package allowing to create
After using this command line, the
r CRANpkg("shiny") interface appear.
Interface's tabs are:
r Rpackage("famat") has four important functions which have to
be used one after another:
data(genes) data(meta) 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) rshiny(compl_data_result)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.