Pathway analysis considers perturbations in biochemical pathways or groups of functionally related molecules (genes, proteins, metabolites etc), instead of individual molecules. Compared to individual molecule analysis, pathway analysis showed some major advantages:
- More informative with up-to-date biology knowledge incorporated in the analysis
- More sensitive in detecting groups of small yet coordinated changes
- More robust and consistent between independent studies or experiments
Pathway analysis is widely applied to essentially all types of high throughput assays or omics data:
- Transcriptomics (microrarray, RNA-seq)
- Genomics (GWAS, SNP, CNV, mutations)
- Epigenomics (DNA methylation, histone modification)
- Proteomics (protein arrays, mass spectrometry)
- Metabolomics (metabolites, compounds, small molecules)
- Metagenomics (environmental genomics, microbiome)
Figure 1: RNA-Seq workflows with GAGE/Pathview.
Pathway analysis is routinely done in our bioinformatics services and collaborative research projects. We have developed some widely used methods and tools for pathway analysis, including GAGE (Generally Applicable Gene-set/Pathway Analysis)  and Pathview (link) (a software package for pathway based data integration and visualization) . These tools can be used by themselves or in integrated workflows (Figure 1).
In addition to tools we developed, we also have access and experience with major open-source and commercial software suites and databases in pathway analysis, including GSEA, Ingenuity Pathway Analysis (IPA), MetaCore, KEGG, Gene Ontology etc.
Figure 2: The activated genes involved in oxidative phosphorylation in sweet potato
1. Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ: GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics 2009, 10:161. (Link)
2. Luo W, Brouwer C: Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 2013, 29(14):1830-1831. (Link)