Detection Of Differential Expression Genes Based On Biological Pathways

Life sciences are experiencing a massive influx of data, exponentially increas-ing the size of databases. Currently, databases contain millions of data sets from transcriptomics and thousands of from proteomics. Differential expression analysis, the comparison of expression level across conditions, has become the primary tool for finding biomarkers, drug targets, and candidates for further research. Typically, gene expression data have been analyzed on a gene-by-gene basis, without regard for complex interactions and association mechanisms. Ignoring the underlying biological information diminishes the power of analysis, obscuring the presence of important biological signals. Therefore, gene set analysis methods is more applicable for high throughput gene data.In this paper, we will study the two-group methods on detection of differentially expressed pathways relating to disease:traditional Hotelling’s T2test, diagonal Hotelling’s T2test and revisional Hotelling’s T2test. Intrinsically, the Hotelling’s T2statistic requires the sample size to be larger than the number of variables. However, for gene set analysis, it is common for the sample size to be much smaller than the number of genes in a set. As a consequence, the Hotelling’s T2statistic is not uniquely defined. Although diagonal Hotelling’s T2statistic ignores the off-diagnoal elements of covariance matrix S can deal with the singular problem, but regard for complex interactions in a gene set. In this paper, I make revisions in Hotelling’s T2statistic, denoted by RT2statistic. It improves the shortage of the above two methods. Compare the proposed methodology with Hotelling’s T2statistic and the diagonal Hotelling’s T2statistic, in the performances of identifying differential pathway, through doing the simulation experiments to create the ROC curves. On simulated data, revisional Hotelling’s T2statistic significantly outperformed the above two methods.