Abstract
We present a multivariate method to find genes with correlated expressions across the samples. Our contributions in this study are three-fold: firstly, we develop a difference vector-based technique which unfolds hidden correlations over a subset of genes, secondly, we present a similarity measure which enables grouping of gene expressions based on local similarity regardless of global distance, and thirdly, we devise visualization tools that are useful for conducting an ‘explainable’ analysis. Integrating these techniques with the spectral clustering algorithm, biomarker genes can be effectively identified. We have evaluated our method on six microarray datasets that are widely used as a testbed. When we apply our method in the sample classification problem as well as gene selection, we can successfully explain the source of misclassification by showing the correlation patterns for a subset of genes with the aid of the visualization tools.
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Pok, G., Jin, C., Namsrai, OE., Ryu, K.H. (2012). Detection of Correlated Microarray Expressions Using Difference Values. In: Lee, G., Howard, D., Ślęzak, D., Hong, Y.S. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Communications in Computer and Information Science, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32692-9_65
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DOI: https://doi.org/10.1007/978-3-642-32692-9_65
Publisher Name: Springer, Berlin, Heidelberg
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