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表征微阵列实验中的染料偏差。

Characterizing dye bias in microarray experiments.

作者信息

Dobbin K K, Kawasaki E S, Petersen D W, Simon R M

机构信息

Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

出版信息

Bioinformatics. 2005 May 15;21(10):2430-7. doi: 10.1093/bioinformatics/bti378. Epub 2005 Mar 17.

Abstract

MOTIVATION

Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable.

RESULTS

We analyzed two large (each >27 arrays) tissue culture experiments with extensive dye swap arrays to better characterize dye bias. Indirect, amino-allyl labeling was used in both experiments. We found that post-normalization dye bias that is consistent across samples does appear to exist for many genes, and that controlling and correcting for this type of dye bias in design and analysis is advisable. The extent of this type of dye bias remained unchanged under a wide range of normalization methods (median-centering, various loess normalizations) and statistical analysis techniques (parametric, rank based, permutation based, etc.). We also found dye bias related to the individual samples for a much smaller subset of genes. But these sample-specific dye biases appeared to have minimal impact on estimated gene-expression differences between the cell lines.

摘要

动机

在双标记微阵列实验中,斑点强度可作为基因表达的替代指标。染料偏差定义为用不同染料标记的样本之间的强度差异,这种差异归因于染料而非样本中的基因表达。未通过阵列归一化消除的染料偏差会在感兴趣的样本比较中引入偏差。但是,如果同一基因在不同样本中的偏差一致,则可以通过适当的实验设计和分析进行校正。如果同一基因在不同样本中的染料偏差不一致,而是因样本而异,那么消除偏差就会变得更成问题,这可能表明荧光信号准确代表基因表达的能力存在技术限制。因此,表征染料偏差以确定以下几点很重要:(1)它是否会通过阵列归一化被所有基因消除;(2)它是否不会被归一化消除,但可以通过适当的实验设计和分析消除;(3)染料偏差校正是否比上述任何一种情况都更成问题且不易消除。

结果

我们分析了两个大型(每个大于27个阵列)的组织培养实验,这些实验有大量的染料交换阵列,以便更好地表征染料偏差。两个实验均使用间接氨基烯丙基标记。我们发现,许多基因在归一化后确实存在样本间一致的染料偏差,并且在设计和分析中控制和校正这种类型的染料偏差是可取的。在广泛的归一化方法(中位数中心化、各种局部加权回归归一化)和统计分析技术(参数法、基于秩的、基于置换的等)下,这种类型的染料偏差程度保持不变。我们还发现,对于一小部分基因存在与单个样本相关的染料偏差。但这些样本特异性的染料偏差似乎对细胞系之间估计的基因表达差异影响最小。

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