| 1. |
- Jay, Jeremy J., et al.
(författare)
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A systematic comparison of genome-scale clustering algorithms
- 2012
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Background: A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods: For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each clusters agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results: Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions: Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further development and application of combinatorial strategies is warranted.
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| 2. |
- Mobini, Reza, 1965, et al.
(författare)
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A module-based analytical strategy to identify novel disease-associated genes shows an inhibitory role for interleukin 7 Receptor in allergic inflammation.
- 2009
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Ingår i: BMC systems biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The identification of novel genes by high-throughput studies of complex diseases is complicated by the large number of potential genes. However, since disease-associated genes tend to interact, one solution is to arrange them in modules based on co-expression data and known gene interactions. The hypothesis of this study was that such a module could be a) found and validated in allergic disease and b) used to find and validate one ore more novel disease-associated genes. RESULTS: To test these hypotheses integrated analysis of a large number of gene expression microarray experiments from different forms of allergy was performed. This led to the identification of an experimentally validated reference gene that was used to construct a module of co-expressed and interacting genes. This module was validated in an independent material, by replicating the expression changes in allergen-challenged CD4+ cells. Moreover, the changes were reversed following treatment with corticosteroids. The module contained several novel disease-associated genes, of which the one with the highest number of interactions with known disease genes, IL7R, was selected for further validation. The expression levels of IL7R in allergen challenged CD4+ cells decreased following challenge but increased after treatment. This suggested an inhibitory role, which was confirmed by functional studies. CONCLUSION: We propose that a module-based analytical strategy is generally applicable to find novel genes in complex diseases.
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