Abstract
Genome-wide differential expression studies of human diseases using microarray technology usually produce long lists of genes with altered expression, therefore, the genes causally involved in a disease cannot be effectively separated from innocent bystanders. Existing methods for differential analysis of gene expression profiles seem unable to solve this problem successfully. In this paper, we present a systematic strategy that combines gene-wise and function-wise differential analysis of gene expression profiles to interrelate genes and functions with human diseases. The gene-wise analysis adopts a modified T-test to analyze the expression alteration of each single gene, and the alteration is represented by quantitative significant p-value. The function-wise analysis uses a new combined S-test to identify coordinate alterations of genes within each functional category. A computational tool, MageKey, is developed based on this strategy, and its utility is demonstrated by the analysis results of gene expression dataset of human Amyotrophic Lateral Sclerosis disease. MageKey is freely available upon request to authors.
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Wang, W., Zhou, Y., Bi, R. (2005). Correlating Genes and Functions to Human Disease by Systematic Differential Analysis of Expression Profiles. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_2
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DOI: https://doi.org/10.1007/11538356_2
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