neaGUI

Network Enrichment Analysis

After discovering list of differentially expressed genes, the next challenge is to interpret them on a way that is consistent with biological hypotheses. It is known that genes work on networks. In humans there are around 22,258 to whom correspondence should be addressed protein-coding genes connected in 650,000 predicted interactions (Minguez and Dopazo, 2010). The knowledge of gene-protein network is now increasingly used as a based for characterizing gene sets. Alexeyenko et al., 2012 proposed a network enrichment analysis (NEA) method which systematically implements the network approach to describe novel gene sets with biologically meaningful functional categories were proposed.

The NEA method integrates functional information and network connectivity of nearly all protein-coding genes and quantifies the over/under-representation of the functional group members among the neighbors in the gene network rather than in the AGS itself. In the NEA methods a fast network randomization algorithm to obtain the distribution of any network statistics under the null hypothesis of no association between an AGS and FGS is used.

neaGUI Package

The neaGUI was developed to perform the network enrichment analysis (NEA) proposed by Alexeyenko et al. (2012) using the R-tcl/tk interface implemented in the R-tcl/tk package (Dalgaard, 2001). The neaGUI requires the following R packages: tcltk, KEGG.db, GO.db, reactome.db, org.Hs.eg.db, AnnotationDbi, and hwriter.

Developer

Setia Pramana (maintainer), Woojoo Lee, Andrey Alexeyenko, and Yudi Pawitan.