Probabilistic retrieval and visualization of biologically relevant microarray experiments
© Caldas et al; licensee BioMed Central Ltd. 2009
Published: 19 October 2009
Repositories of genome-wide expression studies such as ArrayExpress  have been growing rapidly over the last few years and continue to do so. The more experimental data are deposited into these repositories, the more likely it becomes that some of them can provide a meaningful biological context to aid in the planning and analysis of new studies. Retrieval of experiments based on their textual description and experimental design has several shortcomings. First of all, textual description of an experiment or its results is not as information-rich as the actual data itself. Secondly, information about the experimental design alone is only of limited use in retrieving biologically relevant data because it does not reflect the results, which contain the bulk of the information and may reveal unexpected relationships. We introduce novel retrieval methods that incorporate the actual gene expression measurements into the search process, along with visualization tools for interpreting and exploring the results .
We developed a two-stage procedure, first identifying differentially active gene sets in each experiment using a recent nonparametric statistical method , and then combining gene set activation patterns into higher-level structures, so-called biological topics, using a state-of-the-art probabilistic model . The probabilistic formulation enables the use of a natural and rigorous metric for assessing the similarity between two experiments. For interpreting and exploring retrieval results, we have developed visualization methods that also provide insight into the model used to perform the retrieval.
Using a combination of existing and novel methods for modeling and visualizing a heterogeneous collection of gene expression experiments, we were able to decompose and relate experiments via biologically meaningful components. Our approach allows search within a gene expression database to be driven by actual measurement data.
This work was supported by TEKES (grant no. 40101/07). JC, AF and SK are additionally partially supported by PASCAL 2 Network of Excellence, ICT 216886. JC is additionally supported by a doctoral grant from the Portuguese Foundation for Science and Technology (FCT). NG is supported by a PhD fellowship of the European Molecular Biology Laboratory (EMBL).
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