Identifying Genes And Functions Responsible For Human Disease Based On Gene Expression Profiles

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.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 (ALS) disease. MageKey is able to associate relevant biological functions with human disease.The association between disease phenotype and gene expression change is investigated by integrating functional annotation information. Such analysis strategy is able to effectively interrelate genes and functions with human disease, and help generate biology hypothesis. Meanwhile, our results associating genes and functions with human disease illustrate the value of integrating functional annotation information in the analysis of gene expression profiles.