A Robust Rank-Based Method for Detection of Differentially Co-Expressed Gene Sets
Date Issued
2011
Date
2011
Author(s)
Wang, Yueh
Abstract
It is important to find out the susceptible genetic material associated with different conditions in epidemiology. In recent years, lots of methods are developed for identifying genes or SNPs which contribute to the occurrence of complex disease. One kind of procedure, called Gene Set Analysis (GSA), considers a biologically defined gene set as an analyzed unit. GSA and related methods solved a crucial problem in bioinformatics by reducing a large number of variables. But only partial information - mean change, is utilized. The change, but this information may not be detected by the previous developed GSA methods. In this thesis, an R statistic is developed for extracting the information of gene set co-expression change, and the important genes of a gene set - hub genes, are further identified. In simulation study, we examine the robustness property of the R statistic and the hub genes identification. The p53 data is analyzed and several p53 related pathways are identified. Among those identified pathways, many are not detected by the method focusing on the detection of mean change. We have chosen several pathways for further identifying the hub genes, and the making biologically explanation on each pathway. In discussion, the proposed method has the robust property and is powerful in detection of complicated gene sets. However, two limits of the R statistic can not be ignored, the R statistic is unable to detect a condition that the case and control are similar in the correlation rank order, and the efficiency of this method may not good in some condition. Finally, we should note that the higer order co-expression change is still ignored, the possible algorithm for the consideration of higher order interaction is proposed, in the future work it will be important to extend the R statistic to be able to detect higher order co-expression.
Subjects
gene set
gene set analysis
co-expressio
correlation
robustness
Type
thesis
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