FGNet (version 3.0.7)

FGNet-package: Functional gene networks derived from biological enrichment analyses

Description

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.

Details

Package:

FGNet

Type:

Package

Version:

3.0

License:

GPL (>= 2)

References

[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

See Also

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:

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

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

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

Examples

## Not run: 
# # GUI:
# FGNet_GUI()
# ## End(Not run)

# 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)

results <- fea_david(geneList, geneLabels=geneLabels) # email="[e-mail]"

# 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)