In emiliesecherre/famat: Functional analysis of metabolic and transcriptomic data

BiocStyle::markdown()

Introduction

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:

path_enrich : pathways enrichment analysis
interactions : direct interactions and centrality
compl_data : GO terms enrichment analysis and user's elements data extraction
rshiny : use of previous function's data in a shiny interface

Installation

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

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("famat")

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

library(famat)
library(mgcv)

path_enrich

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" function. Pathways enrichment analysis is performed on genes using r CRANpkg("gprofiler2") and on metabolites using r CRANpkg("MPINet").

interactions

"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" 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.

compl_data

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.

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.

rshiny

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.

data(compl_data_result)

rshiny(compl_data_result)

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

test Interface's tabs are:

Elements : Genes, metabolites, interactions, and information related to these elements. Also, filters can be applied to get pathways/enriched GO terms containing (or not) only certain elements.
Pathways : Heatmap showing pathways hierarchies and user's elements in these pathways. Filters can be applied to get pathways corresponding to certain pathways or interactions types, or to filter genes by their type or enriched GO terms. Also, the heatmap can be colored using interactions types or centrality. Finally, click on a heatmap's cell makes a pop up appear, with information about the pathway selected.
GO Molecular Function : Enriched GO MF terms hierarchies with information about them. Click on a GO term makes a pop up appear with data about the GO term selected.
GO Biological Process : Enriched GO BP terms hierarchies with information about them. Click on a GO term makes a pop up appear with data about the GO term selected.
History : Every time a filter is applied, new results are saved in history. So, user can go back if a mistake was done.
Elements not in pathways : some user's elements may not be in pathways obtained by pathways enrichment analysis and filtered by compl_data function. So, they are printed in this tab to show they were taken into account.

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:

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)

References

Yanjun Xu, Chunquan Li and Xia Li (2013). MPINet: The package can implement the network-based metabolite pathway identification of pathways.. R package version 1.0. https://CRAN.R-project.org/package=MPINet
Liis Kolberg and Uku Raudvere (2020). gprofiler2: Interface to the 'g:Profiler' Toolset. R package version 0.2.0. https://CRAN.R-project.org/package=gprofiler2
Luna, A., Babur, O., Aksoy, A. B, Demir, E., Sander, C. (2015). “PaxtoolsR: Pathway Analysis in R Using Pathway Commons.” Bioinformatics.
Sales G, Calura E, Cavalieri D, Romualdi C (2012). “graphite - a Bioconductor package to convert pathway topology to gene network.” BMC Bioinformatics. https://bmcbioinformatics.biomedcentral.com/articles/10 . 1186/1471-2105-13-20.
Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 2012, 16(5):284-287