Calibration and assessment of channel-specific biases in microarray data with extended dynamical range -: art. no. 177
Calibration and assessment of channel-specific biases in microarray data with extended dynamical range -: art. no. 177
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DOI：
10.1186/1471-2105-5-177
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发表时间：
2004-11-12
影响因子：
3
通讯作者：
Vallon-Christersson, J
中科院分区：
文献类型：
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作者：
Bengtsson, H;Jönsson, G;Vallon-Christersson, J
背景资料：观察到的基因表达对数比的非线性，也称为强度依赖性对数比，通常可以通过比较的两个通道中的全局偏差来解释。在微阵列过程中的任何一步都可能引入这样的偏移，在这篇文章中，我们研究了由微阵列扫描仪和图像分析software.Results引入的偏差：通过扫描相同的斑点寡核苷酸微阵列在不同的光电倍增管（PMT）的增益，我们已经确定了一个通道特定的偏差存在于双通道微阵列数据。对于分析的扫描仪，其范围为15 - 25（共65，535）。尽管PMT增益被大大调整，但在同一阵列的后续扫描之间观察到的偏置非常稳定。这表明偏差不是源于扫描仪检测器部件之前的步骤。阵列之间的偏差略有不同。当比较基于来自相同阵列但来自不同扫描仪的数据的估计值时，我们发现不同扫描仪会引入不同的偏差量。各种图像分析方法。我们提出了一个扫描协议和一个约束仿射模型，使我们能够识别和估计每个通道中的偏差。向后变换可以消除偏差，并使通道达到相同的比例。其结果是，系统的影响，如强度依赖的对数比被删除，而且信号密度变得更加相似。平均扫描，它有一个更大的动态范围和更大的信号-噪声比个别扫描，然后可以得到.Conclusions：研究表明，微阵列扫描仪可能会引入一个显着的偏差在每个通道。必须对此类偏倚进行校准，否则将观察到系统效应，如强度依赖性对数比。建议的扫描协议和校准方法是简单的使用，是有用的评估扫描仪的偏差或获得校准的测量与扩展的动态范围和更好的精度。跨平台的R软件包aroma实现了所有描述的方法，可从http://www.maths.lth.se/ bioinformatics/免费获得。
Background: Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software.Results: By scanning the same spotted oligonucleotide microarray at different photomultiplier tube (PMT) gains, we have identified a channel-specific bias present in two-channel microarray data. For the scanners analyzed it was in the range of 15 - 25 ( out of 65,535). The observed bias was very stable between subsequent scans of the same array although the PMT gain was greatly adjusted. This indicates that the bias does not originate from a step preceding the scanner detector parts. The bias varies slightly between arrays. When comparing estimates based on data from the same array, but from different scanners, we have found that different scanners introduce different amounts of bias. So do various image analysis methods. We propose a scanning protocol and a constrained affine model that allows us to identify and estimate the bias in each channel. Backward transformation removes the bias and brings the channels to the same scale. The result is that systematic effects such as intensity dependent log-ratios are removed, but also that signal densities become much more similar. The average scan, which has a larger dynamical range and greater signal-to-noise ratio than individual scans, can then be obtained.Conclusions: The study shows that microarray scanners may introduce a significant bias in each channel. Such biases have to be calibrated for, otherwise systematic effects such as intensity dependent log-ratios will be observed. The proposed scanning protocol and calibration method is simple to use and is useful for evaluating scanner biases or for obtaining calibrated measurements with extended dynamical range and better precision. The cross-platform R package aroma, which implements all described methods, is available for free from http://www.maths.lth.se/ bioinformatics/.