Meta-analysis is a type of statistical technique used to integrate multiple independent datasets that have been collected under similar experimental conditions, in order to obtain more robust conclusions or biomarkers. By combining multiple data sets, the approach can increase statistical power (more samples) and reduce potential bias.
A key concept in meta-analysis is that it is generally not advisable to directly combine different independent datasets (i.e. merge them into a single large table) and analyze them as a single unit. This is due to potential batch effects associated with each datasets, which can overwhelm the biological effects. This issue has been well-studied in microarray experiments generated from different platforms.
To address this issue, meta-analysis is usually computed based on summary statistics (p values, effect sizes, etc.). The meta-analysis module in ExpressAnalyst was developed to support these common practices. In addition, it also offers heatmaps and other visualization tools to allow users to explore patterns across different studies.