跨平台微阵列数据集成的归一化线性变换

Huilin Xiong, Ya Zhang, Xue-wen Chen
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引用次数: 1

摘要

随着微阵列数据的急剧积累,整合相关研究的数据是增加样本量的自然方法,从而可以进行更可靠的统计分析。然而,不同微阵列平台之间的内在差异使得数据集成不是一项简单的任务。在本文中,我们提出了一种简单而有效的集成方案,称为归一化线性变换(NLT),以组合来自不同微阵列平台的数据。在分类分析和基因标记选择两项任务上,将NLT方案与其他三种集成方案进行了比较。我们的实验表明,在各种分类设置下,NLT方案在分类精度方面表现最好,并导致更多具有生物学意义的标记基因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Normalized Linear Transform for Cross-Platform Microarray Data Integration
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. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized 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 under various classification settings, and leads to more biologically significant marker genes.
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