Abstract
Introduction: Gene-set analysis of microarray data determines biological pathways or gene setswith differential expression in a phenotype of interest. In contrast to the analysis of individual genes, gene-set analysis utilizes existing biological knowledge of genes in assessing differential expression. This paper compared the biological performance of two gene-set analysis methods. Materials and Methods: To determinegene sets, which are differentially expressed between acute lymphoblastic leukemia (ALL) with BCR/ABL and those with no observed cytogenetic abnormalities, the real microarray data come from a clinical trial in acute lymphoblastic leukaemia were used in this study. For this reason, we used two GSA methods GSEA-category and Global test and then the data were analyzedusing by R software. Results: Globaltest identified 114 out of 200 gene sets introduced in KEGG with differentially expressed on comparing the group with BCR/ABL to those with no observed cytogenetic abnormalities. While Category could identify just 30 gene sets of this set. Conclusion: Both of used methods include number of gene sets affecting ALL. So the more thorough study is needed to identify the more metric method. Evaluation of common gene sets among the two methods could identify the latest findings of biologists only by the used statistical methods
Keywords
Gene set, Microarray, Acute lymphoblastic leukemia, Globaltest, Category خانواده ژنی، ریزآرایه، لوسمی لنفوبلاستیک حاد، فراموضعی، طبقهای
Copyright
© 2013, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.