aiSee: Analysis of Gene Expression Experiments

Graph-based iterative group analysis of gene expression experiments

Visualization of up-regulated gene neighborhoods identified by graph-based iterative group analysis of the classic yeast diauxic shift experiment by DeRisi et al. Red boxes show the regulated genes (darker shading indicates stronger regulation), white boxes show the substrates that are in common between genes. Grey boxes represent folded subgraphs. Courtesy of R. Breitling, A. Amtmann and P. Herzyk, University of Glasgow.

“One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression.

At the University of Glasgow, we applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. This approach provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.

We chose the aiSee software for our graph layout, because it was very easy to learn and yet provided all the flexibility to produce very informative, data-rich graphs. The generated pictures provide a powerful tool to explore the large multidimensional datasets ”produced by modern post-genomic technologies, such as gene expression arrays or metabolomics. Our biologist customers just love it.

Dr. Rainer Breitling, University of Glasgow