Functional visualisation of genes using singular value decomposition

Authors:
Hamid Ghous;Paul J. Kennedy;Nicholas Ho;Daniel R. Catchpoole
Affiliations:
University of Technology, Sydney, Broadway NSW, Australia;University of Technology, Sydney, Broadway NSW, Australia;The Kid's Research Institute, The Children's Hospital at Westmead, Westmead NSW, Australia;The Kid's Research Institute, The Children's Hospital at Westmead, Westmead NSW, Australia
Venue:
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Year:
2012

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Abstract

Progress in understanding core pathways and processes of cancer requires thorough analysis of many coding regions of the genome. 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), has the potential to assist in functional understanding. This paper addresses the question of how well GO annotations can help in functional understanding of genes. We augment genes with associated GO terms and visualise with Singular Value Decomposition (SVD). Meaning of derived components is further interpreted using correlations to GO terms. The results demonstrate that SVD visualisation of GO--augmented genes matches the biological understanding expected in the simulated data and presents understanding of childhood cancer genes that aligns with published results.