FGNet-package: Functional gene networks derived from biological enrichment...
In FGNet: Functional Gene Networks derived from biological enrichment analyses

Description Details Author(s) References See Also Examples

Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO.

Package:	FGNet
Type:	Package
Version:	3.0
License:	GPL (>= 2)

Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain.

If you have any issue, you can contact us at: <jrivas at usal.es>

[1] Fontanillo C, Nogales-Cadenas R, Pascual-Montano A, De Las Rivas J (2011) Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms. PLoS ONE 6(9): e24289. URL: http://gtlinker.cnb.csic.es

[2] Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37(1):1-13. URL: http://david.abcc.ncifcrf.gov/

[3] Alexa A, and Rahnenfuhrer J (2010) topGO: Enrichment analysis for Gene Ontology. R package version 2.16.0. URL: http://www.bioconductor.org/packages/release/bioc/html/topGO.html

[4] Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ (2009) GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics. 10:161. URL: http://www.bioconductor.org/packages/release/bioc/html/gage.html

FGNet_GUI() provides a Graphical User Interface (GUI) to most of the functionalities of the package: Performing a Functional Enrichment Analysis (FEA) of a list of genes, and analyzing it through the functional networks.

1. The Functional Enrichment Analysis can be performed through several tools:

DAVID [1]: fea_david() (Requires internet connection)
GeneTerm Linker [2]: fea_gtLinker() & fea_gtLinker_getResults() (Requires internet connection)
topGO [3]: fea_topGO() (Only supports GO. For offline use requires having installed the required database packages)
GAGE [4]: fea_gage() (GSEA analysis. For offline use requires gene sets or installed database packages)

There are also a few functions to import the results from a previous FEA analysis: format_david(), format_results() and readGeneTermSets().

2. FGNet_report(): automatically generates a report with the default network options. It includes the following steps, wich can be executed individually to personalize or explore the networks:

fea2incidMat(): Transforms the FEA output into incidence matrices. These function determines wether the network will be gene- or term-based.
functionalNetwork(): Generates and plots the functional networks. These networks can be further explored by analyzeNetwork() and clustersDistance().

Other auxiliary functions: getTerms(), keywordsTerm(), plotGoAncestors()

For more info see the package tutorial: vignette("FGNet-vignette")

## Not run: 
# GUI:
FGNet_GUI()


# 1. FEA:
geneList <- c("YBL084C", "YDL008W", "YDR118W", "YDR301W", "YDR448W", "YFR036W", 
    "YGL240W", "YHR166C", "YKL022C", "YLR102C", "YLR115W", "YLR127C", "YNL172W", 
    "YOL149W", "YOR249C")
    
library(org.Sc.sgd.db)
geneLabels <- unlist(as.list(org.Sc.sgdGENENAME)[geneList])

# Optional: Gene expression 
geneExpr <- setNames(c(rep(1,10),rep(-1,5)), geneLabels)

# Choose FEA tool...
# results <- fea_david(geneList, geneLabels=geneLabels, email="example@email.com")
results <- fea_gtLinker_getResults(jobID=3907019)

# 2 A) Report:
FGNet_report(results, geneExpr=geneExpr)

# 2 B) Step by step:
# 2.1. Create incidence matrices:
incidMat <- fea2incidMat(results)
incidMat_terms <- fea2incidMat(results, key="Terms")

# 2.2. Explore networks:
functionalNetwork(incidMat, geneExpr=geneExpr)
functionalNetwork(incidMat_terms, plotType="bipartite", plotOutput="dynamic")
getTerms(results)

nwStats <- analyzeNetwork(incidMat)
clustersDistance(incidMat)

## End(Not run)