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Gene expression profiling has become an important tool for biological research, integrative analysis of microarray data is useful for some researches. Typical integrative approach at gene level is meta-analysis. Recently, Gene Set Enrichment Analysis (GSEA) has been widely applied to bring gene level interpretation to pathway level. The objective of this webserver is to combine meta-analysis and GSEA to extract consistent expression pattern change from multiple microarray datasets at pathway level.
In this tool, meta-analysis is used as the first step to integrate microarray studies from different labs on a similar biological problem. As an output of meta-analysis, one dataset is obtainedthat is consisted of transformed Z scores for each gene. Z score of a gene stands for its overall expression change in original biological samples integrated from multiple datasets. The ranked list of Z scores that is related to the phenotypes of two classes is then given as input for GSEA software. Using GSEA, the enriched significantly changed gene sets (pathways) can be obtained.
