Microarray-based gene set analysis: a comparison of current methods Export

@article{citeulike:3718551, abstract = {BACKGROUND:The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.RESULTS:Each of six gene set analysis methods was applied to both simulated and publicly available microarray data sets. Overall, the various methodologies were all found to be better at detecting gene sets that moved from non-active (i.e., genes not expressed) to active states (or vice versa), rather than those that simply changed their level of activity. Methods which incorporate correlation structures were found to provide increased ability to detect altered gene sets in some settings.CONCLUSION:Based on the results obtained through the analysis of simulated data, it is clear that the performance of gene set analysis methods is strongly influenced by the features of the data set in question, and that methods which incorporate correlation structures into the analysis process tend to achieve better performance, relative to methods which rely on univariate test statistics.}, author = {Song, Sarah and Black, Michael}, citeulike-article-id = {3718551}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-502}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19038052}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19038052}, doi = {10.1186/1471-2105-9-502}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {gene-expression, methods, microarray, reviews}, number = {1}, pages = {502+}, posted-at = {2008-12-31 23:34:31}, priority = {2}, title = {Microarray-based gene set analysis: a comparison of current methods}, url = {http://dx.doi.org/10.1186/1471-2105-9-502}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:3718551 L3 - citeulike-article-id:3718551 N2 - BACKGROUND:The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.RESULTS:Each of six gene set analysis methods was applied to both simulated and publicly available microarray data sets. Overall, the various methodologies were all found to be better at detecting gene sets that moved from non-active (i.e., genes not expressed) to active states (or vice versa), rather than those that simply changed their level of activity. Methods which incorporate correlation structures were found to provide increased ability to detect altered gene sets in some settings.CONCLUSION:Based on the results obtained through the analysis of simulated data, it is clear that the performance of gene set analysis methods is strongly influenced by the features of the data set in question, and that methods which incorporate correlation structures into the analysis process tend to achieve better performance, relative to methods which rely on univariate test statistics. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Microarray-based gene set analysis: a comparison of current methods VL - 9 SP - 502 KW - gene-expression KW - methods KW - microarray KW - reviews AU - Song, Sarah AU - Black, Michael PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-502 ER -