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Probabilistic retrieval and visualization of biologically relevant microarray experiments
BMC Bioinformatics volume 10, Article number: P1 (2009)
Background
Repositories of genome-wide expression studies such as ArrayExpress [1] 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 [2].
Methods
We developed a two-stage procedure, first identifying differentially active gene sets in each experiment using a recent nonparametric statistical method [3], and then combining gene set activation patterns into higher-level structures, so-called biological topics, using a state-of-the-art probabilistic model [4]. 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.
Results
We show that gene sets corresponding to each biological topic form highly coherent and holistic components. Several case studies performed on a subset of ArrayExpress show that our method can retrieve experiments relevant to a biological question, as long as sufficient amounts of data are available, and highlight relations between experiments, either because the same biological questions were targeted, or because of unexpected relationships that were confirmed in the literature. The visualization methods allow us to both efficiently interpret the model and put retrieval results in the context of the whole set of experiments (see Figure 1 for an example).
2D NeRV projection of retrieval results when the model is queried with a malignant melanoma experiment. Each experiment is represented as a striped glyph. Colors indicate biological topics. Stripe widths indicate the predominance of each biological topic in each experiment. Glyph size indicates relevance to the malignant melanoma query experiment [5].
Conclusion
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.
References
Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Brube H, Dylab M, Emam I, Farne A: ArrayExpress update – from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res 2007, 37(Database Issue):D868-D872.
Caldas J, Gehlenborg N, Faisal A, Brazma A, Kaski K: Probabilistic retrieval and visualization of biologically relevant microarray experiments. Bioinformatics 2009, 25():i145-i153. 10.1093/bioinformatics/btp215
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gilette MA, Paulovich A, Pomeroy SL, Golub TR, et al.: Gene set enrichment analysis – a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102: 15545–15550. 10.1073/pnas.0506580102
Blei D, Ng A, Jordan MI: Latent Dirichlet Allocation. J Mach Learn Res 2003, 3: 993–1022. 10.1162/jmlr.2003.3.4-5.993
Venna J, Kaski S, Jordan MI: Nonlinear dimensionality reduction as information retrieval. AISTATS'07 2007.
Acknowledgements
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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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Caldas, J., Gehlenborg, N., Faisal, A. et al. Probabilistic retrieval and visualization of biologically relevant microarray experiments. BMC Bioinformatics 10 (Suppl 13), P1 (2009). https://doi.org/10.1186/1471-2105-10-S13-P1
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DOI: https://doi.org/10.1186/1471-2105-10-S13-P1
Keywords
- Textual Description
- Visualization Method
- Gene Expression Measurement
- Biological Question
- Actual Gene Expression