Background:
Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform
enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype.
Objective:
In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression
and highly co-related pathways.
Methods:
We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends
or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations
(dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets
on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods.
Result and Conclusion:
We also combine between-gene set CIA and GSEA to discover the relationships between gene
sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate
integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using
the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization
of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.
Novel gene sets improve set-level classification of prokaryotic gene expression data
