We have developed a system to classify cellular forms of fusion proteins with an enhanced yellow fluorescent protein (EYFP) into subcellular compartments using images by a fluorescent microscope. The system aims at image classification to accommodate the multiplicity of cellular forms. The cellular forms automatically searched for in the images are classified with excellent reliability using statistical pattern recognition. The patterns are extended models of the cellular forms which are the result of protein localization as seen in seven standard subcellular compartments, including in the apoptosis and the overexpression. The image itself is classified by the majority of cellular forms it contains so as to reflect the characteristics of cell population, rejecting cellular forms with low reliability and in the apoptosis. We have found that our system is 97.9% accurate in classifying cellular forms into subcellular compartments.

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In recent years, the DNA microarray has become one of the most powerful tools for toxicogenomic studies. However, microarray technology is still in its early stages, and standardized analytical methods of cDNA microarray analysis have not been clearly established. These differences in methodology can result in data variability in gene expression profiling. Although many analytical methods have been proposed to resolve these problems, they are not practical methods in the field of ecotoxicology because environmental samples are limited and microarray experiments are quite expensive. Here we examined the basic analytical methods of cDNA microarrays using yeast cells to standardize yeast cDNA microarray technology for toxicogenomics. We attempted to appear practical methods to obtain reliable yeast cDNA microarray data from a minimum number of experiments. We propose that our experimental conditions (exponentially growing yeast cells (A₆₆₀=1.0) in YPD medium) were reproducible for cDNA microarray experiments with a correlation factor of approximately 9.0. In addition, reliable data was obtained when we selected induced genes whose expression levels increased more than 2.0-fold in at least two of the three independent experiments.

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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.

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