Comparison of Univariate and Multivariate Gene Set Analysisin Acute Lymphoblastic Leukemia


Background: Gene set analysis (GSA) incorporates biological with statistical knowledge to identify gene setswhich are differentially expressed that between two or more phenotypes. Materials and
Methods: In this papergene sets differentially expressed between acute lymphoblastic leukaemia (ALL) with BCR-ABL and those withno observed cytogenetic abnormalities were determined by GSA methods. The BCR-ABL is an abnormal genefound in some people with ALL.
Results: The results of two GSAs showed that the Category test identified 30gene sets differentially expressed between two phenotypes, while the Hotelling’s T2 could discover just 19 genesets. On the other hand, assessment of common genes among significant gene sets showed that there were highagreement between the results of GSA and the findings of biologists. In addition, the performance of these methodswas compared by simulated and ALL data.
Conclusions: The results on simulated data indicated decrease inthe type I error rate and increase the power in multivariate (Hotelling’s T2) test as increasing the correlationbetween gene pairs in contrast to the univariate (Category) test.