Introduction
What is new?
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DNA microarrays became a widely used tool in the biomedical research and testing ground for novel statistical methodologies.
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Many biological, technological, statistical and informatics challenges exist.
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Gene expression profiling, when used properly, offers the potential for development of clinically-relevant biomarkers.
Microarray technology has become a widely used tool for genome-wide gene expression profiling, where expression levels of thousands of genes are measured at once. It is hoped that, by analyzing patterns of gene expression (e.g., profiling), scientists will be able to better understand the molecular etiology of multifactorial disorders, such as obesity, diabetes, heart disease, or cancer. Microarray technology offers an opportunity to pinpoint a few genes that may be the “key players” in the observable biological phenomena as well as to view a “big picture” and reveal important multigene interactions and understand changes at the level of molecular pathways and networks.
However, major challenges exist. Successful microarray experiment requires proper planning and sound experimental design that accounts for various sources of variability; careful sampling and preparation of biological material; thorough array processing, hybridization, scanning, and image analysis. The application of different analytical approaches to these massive data sets can result in different outcomes. Additionally, comparison of results obtained using different microarray platforms remains a challenge because of various informatic issues. Thus, the success of microarray studies depends not only on the quality of experimental design and data but also on the statistical and bioinformatic methods of analysis. Herein, we discuss advances in gene expression–profiling studies over the last decade and identify areas that need further research. There are many related fields where transcriptional data are used that we do not cover, such as eQTL studies, systems biology, or genome-wide association studies [1], [2], [3].