Abstract
Along with the developments and advances in microarray technology, data analysis is becoming an increasingly critical step of the microarray system for unraveling complicated biological mechanisms. As there are various platforms for microarray technology and microarrays are used for different purposes, many methods have been consequently devised for data analyzing. It is not easy, however, to choose the most appropriate method for each situation. This review focuses on the currently available methods for "input" and "output" data processing, including normalization in raw data processing and the use of ontology and meta-analysis in data aggregation. By presenting detailed explanations of both the major established methods and several state-of-the-art approaches, this review aims to provide a brief overview of the trends in microarray data analysis.
