Adjustment of systematic microarray data biases
Adjustment of systematic microarray data biases
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DOI：
10.1093/bioinformatics/btg385
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发表时间：
2004-01-01
期刊：
影响因子：
5.8
通讯作者：
Marron, JS
中科院分区：
文献类型：
--
作者：
Benito, M;Parker, J;Marron, JS
动机：由于微阵列实验的实验特点，在大多数大型微阵列数据集中都存在系统差异。许多不同的实验特征会导致偏差，包括不同的RNA来源、不同的生产批量的微阵列或不同的微阵列平台。这些系统效应给微阵列数据的分析带来了巨大的障碍。结果：我们提出了一种新的方法来识别和调整存在于微阵列数据集中的系统偏差。我们的方法是基于现代统计判别方法，并被证明非常有效地消除了存在于先前发表的乳腺肿瘤基因芯片数据集中的系统性偏差。结果表明，距离加权判别法比支持向量机和奇异值分解法更适合于系统微阵列效应的调整。此外，它被证明是一种普遍使用的工具，用于区分微阵列数据集中存在的系统性问题，包括合并在不同微阵列平台上完成的两个乳腺肿瘤数据集。
Motivation: Systematic differences due to experimental features of microarray experiments are present in most large microarray data sets. Many different experimental features can cause biases including different sources of RNA, different production lots of microarrays or different microarray platforms. These systematic effects present a substantial hurdle to the analysis of microarray data.Results: We present here a new method for the identification and adjustment of systematic biases that are present within microarray data sets. Our approach is based on modern statistical discrimination methods and is shown to be very effective in removing systematic biases present in a previously published breast tumor cDNA microarray data set. The new method of 'Distance Weighted Discrimination (DWD)' is shown to be better than Support Vector Machines and Singular Value Decomposition for the adjustment of systematic microarray effects. In addition, it is shown to be of general use as a tool for the discrimination of systematic problems present in microarray data sets, including the merging of two breast tumor data sets completed on different microarray platforms.