Pathway-based visualization of cross-platform microarray datasets

Motivation: Traditionally, microarrays were almost exclusively used for the genome-wide analysis of differential gene expression. But nowadays, their scope of application has been extended to various genomic features, such as microRNAs (miRNAs), proteins and DNA methylation (DNAm). Most available methods for the visualization of these datasets are focused on individual platforms and are not capable of integratively visualizing multiple microarray datasets from cross-platform studies. Above all, there is a demand for methods that can visualize genomic features that are not directly linked to protein-coding genes, such as regulatory RNAs (e. g. miRNAs) and epigenetic alterations (e. g. DNAm), in a pathway-centred manner. Results: We present a novel pathway-based visualization method that is especially suitable for the visualization of high-throughput datasets from multiple different microarray platforms that were used for the analysis of diverse genomic features in the same set of biological samples. The proposed methodology includes concepts for linking DNAm and miRNA expression datasets to canonical signalling and metabolic pathways. We further point out strategies for displaying data from multiple proteins and protein modifications corresponding to the same gene. Ultimately, we show how data from four distinct platform types (messenger RNA, miRNA, protein and DNAm arrays) can be integratively visualized in the context of canonical pathways.