Abstract
BackgroundOne of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes.ResultsWe 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.ConclusionROKU 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.
Highlights
One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others
Genes over-expressed in a small number of tissues but unexpressed or slightly expressed in others, such as those shown in Figs. 1a and 1c, are defined as tissue-specific genes in a narrow sense, while genes over- and/or underexpressed in a small number of tissues compared to other tissues are defined as tissue-specific in a broad sense
The range of H is from 0 whose gene expression is perfectly restricted in a single tissue (Fig. 1a) to log2(N) whose gene expression pattern is flat in all the interrogated tissues (Fig. 1b)
Summary
One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. 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]. We observed that two of the top five probesets specific to liver were found in the top five probesets specific to gall bladder [4] The issue of such redundancies is a concern with any ranking-based method, such as patternmatching [2], when the number of interrogated tissues (page number not for citation purposes)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
