Pathway maps enable straightforward yet customized and semi-automated yet insightful analyses of omics data

To explore the molecular processes underlying some biological theme of interest based on public data, gene lists are used herein as input for the construction of annotated pathway maps, employing Cytoscape apps, and then high-throughput (“omics”) gene expression data are overlaid onto these maps. Seeded with a published set of marker genes of the senescence-associated secretory phenotype and the genes of the cellular senescence KEGG pathway, a gene/protein interaction network and annotated clusters (a “pathway map”) of cellular senescence are derived. The map can be amended, by adding some application-specific genes, and overlaid with gene expression data describing cellular senescence of fibroblasts and with disease-related gene expression data associated with prostate and pancreatic cancer, and with ischemic stroke, allowing insights into the role of cellular senescence in disease. Some gene expression data are derived from the “Biomarker Benchmark repository”. The pathway map approach can be followed in principle for any biological theme of interest, fostering much-needed independence from the investigator-biased expert networks usually used for overlaying gene expression data.