Title: Cross-platform microarray data integration using the Normalised Linear Transform

Authors: Huilin Xiong, Ya Zhang, Xue-Wen Chen, Jiangsheng Yu

Addresses: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. ' Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. ' Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. ' Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA; School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

Abstract: Small sample size is one of the biggest challenges in microarray data analysis. With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. In this paper, we present a simple and effective integration scheme, called Normalised Linear Transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy, and leads to more biologically significant marker genes.

Keywords: microarray data analysis; microarray data integration; normalised linear transform; data classification; KNN; K nearest neighbour; SVMs; support vector machines; feature selection; gene marker selection; NLT; normalised linear transform; classification accuracy; marker genes.

DOI: 10.1504/IJDMB.2010.032168

International Journal of Data Mining and Bioinformatics, 2010 Vol.4 No.2, pp.142 - 157

Published online: 11 Mar 2010 *

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