The rapid advances in
high-throughput technologies, such as microarrays have revolutionizing the
knowledge and understanding of biological systems and genetic signatures of human diseases. This has led to the generation and accumulation of a large
amount of genomic data that need to be adequately integrated to obtain more
reliable and valid results than those from individual experiments.
Meta-analysis of microarray data is one of the most common statistical
techniques used for combining multiple data sets. Despite its remarkable successes in discovering molecular sub types, underlying pathways and bio markers for the pathological process of interest, this method possesses several
limitations.
Here, we provided a briefly
overview of current meta-analytic approaches together with the basic critical
issues in performing meta-analysis of genomic data, with the aim of helping
researchers to evaluate the quality of existing, published data and obtain more
detailed information on what will be the best strategy to adopt to execute a
good meta-analysis.
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