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. 2006 Jun 12:7:294.
doi: 10.1186/1471-2105-7-294.

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

Koji Kadota  1 , Jiazhen Ye, Yuji Nakai, Tohru Terada, Kentaro Shimizu
Affiliations

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

Koji Kadota et al. BMC Bioinformatics. .

Abstract

Background: 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. We would like to have ways of identifying such tissue-specific genes.

Results: 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.

Conclusion: 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.

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Figures

Figure 1
Figure 1
Shannon entropy calculation for various tissue-specific expression patterns. Synthetic expression patterns are shown. Original expression data represented by black circles are processed by equation 2 using Tukey biweights (dashed line). Processed data are represented by red circles. Specific expression observations detected to be outliers by [3] are highlighted. Numbers in black and red indicate Shannon entropy scores for the original and the processed data, respectively. Shannon entropy can range from (a) 0 to (b) 3.32 in this case (logarithm to the base 2 of 10). Expression patterns such as (a) and (c) are defined as tissue-specific genes in a narrow sense. Tissue-specific genes in a broad sense include various expression patterns such as (a, c, d) up-type, (e) down-type, and (f) mixed-type. By virtue of data processing, ROKU can detect tissue-specific genes in a broad sense. Meanwhile, ROKU gives relatively high scores (close to 3.32) for non-specific gene expression patterns such as (g) and (h).
Figure 2
Figure 2
Expression patterns of probesets listed in Table 1. Expression patterns of probesets with the two (a) lowest- and (b) highest (H(x') - H(x)) scores are shown. Other legends are the same as given in Fig. 1.
Figure 3
Figure 3
Expression patterns of probesets listed in Table 2. Expression patterns of top-ranked probesets specific to (a) lung and to (b) fetal lung are shown. Other legends are the same as given in Fig. 1. Note that the two methods output different top-ranked probesets and probesets detected by ROKU are specific only to the objective tissue.
Figure 4
Figure 4
Effect of different quantification algorithms on gene ranking. MAS5- and RMA-quantified data are compared. The higher the percentage of common probesets between the two, the more rank-invariant property the method has. ROKU gives a more invariant gene ranking than Schug's method.
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