Comparing functional visualisations of lists of genes using singular value decomposition

Progress in understanding core pathways of cancer requires analysis of many genes. New insights are hampered due to the lack of tools to make sense of large lists of genes identified using high throughput technology. Data mining, particularly visualisation that finds relationships between genes and the Gene Ontology (GO), can assist in functional understanding. This paper addresses the question using GO annotations for functional understanding of genes. We augment genes with GO terms using two similarity measures: a Hop-based measure and an Information Content based measure, and visualise with Singular Value Decomposition (SVD). The results demonstrate that SVD visualisation of GO augmented genes matches the biological understanding expected in simulated and real-life data. Differences are observed in visualisation of GO terms, where the information content method produces more tightly-packed clusters than the hop-based method.