Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

Abstract

Background: Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.

Results: We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.

Conclusions: The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

Figure 1

The distribution of the p-values of the statistics computed from the randomized network. If the randomization works well, then the distribution should be uniform.

Figure 2

A schematic diagram of counting network links and a real example of network links.(A) A simple example of how links are counted between genes in AGS and FGS. The ’x’ symbol indicates a fixed number k of genes; (B) A realistic example of network links between 257 deregulated proteins in a tumor (diamonds) and 10 genes known to be involved in the epithelial-mesenchymal transition (circles). Network nodes without links to the AGS or FGS genes are not shown. The graph is generated using a graphics tool in the FunCoup web site (http://funcoup.sbc.su.se).

Figure 3

Average of estimated FDRs versus the number of AGS-FGS pairs that are declared significant.(a) FNEA (solid) versus GEA (dashed) (b) MNEA (solid) and GSEA (dashed). The average values were calculated over 123 individuals.