ROKU: a novel method for identification of tissue-specific genes

We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues.ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.A major challenge of microarray analysis is to detect genes whose expression in a single or small number of tissues is significantly different than in other tissues. Accurate identification of such tissue-specific genes can allow researchers to deduce the function of their tissues and organs at the molecular level [1].Several methods have been used for this purpose [1-5]. Of these, Schug et al. [4] demonstrated the effectiveness of using Shannon information theoretic entropy for ranking genes according to their tissue-specificity, from restricted (tissue-specific) expression to average (ubiquitous/housekeeping) expression. However, there is also a severe disadvantage. The entropy does not explain to which tissue a gene is tissue-specific, only measuring the degree of overall tissue specificity of the gene. Hence further analysis to identify specific tissues is needed. Although Schug et al. [4] proposed a new statistic (Q) based on entropy to estimate the degree of a gene's specificity on a particular tissue, the issue of redundancies remains where top-ranked genes as specific to tissue A are also top-ranked as specific t