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Detection of genes with tissue-specific expression patterns using Akaike’s information criterion procedure

¹ Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064 Japan
² Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama Institute, Yokohama 230-0045 Japan
³ Department of Biotechnology, University of Tokyo, Tokyo 113-8657 Japan

We applied a method based on Akaike’s information criterion (AIC) to detect genes whose expression profile is considerably different in some tissue(s) than in others. Such observations are detected as outliers, and the method we used was originally developed to detect outliers. The main advantage of the method is that objective decisions are possible because the procedure is independent of a significance level. We applied the method to 48 expression ratios corresponding to various tissues in each of 14,610 clones obtained from the RIKEN Expression Array Database (READ; http://read.gsc.riken.go.jp). As a result, for several tissues (e.g., muscle, heart, and tongue tissues that contain similar cell types) we objectively obtained specific clones without any "thresholding." Our study demonstrates the feasibility of the method for detecting tissue-specific gene expression patterns.

outlier detection; tissue-specific expression; DNA microarray; AIC; expression analysis

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