Characterizing dye bias in microarray experiments
Characterizing dye bias in microarray experiments
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DOI：
10.1093/bioinformatics/bti378
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发表时间：
2005-05-15
期刊：
影响因子：
5.8
通讯作者：
Simon, RM
中科院分区：
文献类型：
--
作者：
Dobbin, KK;Kawasaki, ES;Simon, RM
动机：斑点强度在双标记微阵列实验中作为基因表达的代理。染料偏倚定义为用不同染料标记的样品之间的强度差异，其可归因于染料而不是样品中的基因表达。未通过阵列归一化去除的染料偏差可能会将偏差引入感兴趣的样品之间的比较中。但是，如果同一基因的样本之间的偏差是一致的，它可以通过适当的实验设计和分析来纠正。如果对于相同基因，染料偏差在样品之间不一致，但对于不同样品是不同的，则去除偏差变得更成问题，这可能表明荧光信号准确表示基因表达的能力的技术限制。因此，重要的是表征染料偏差，以确定：（1）是否通过阵列归一化将所有基因的染料偏差去除，（2）是否通过归一化将染料偏差去除，但可以通过适当的实验设计和分析去除，以及（3）染料偏差校正是否比这两种方法中的任何一种都更有问题，并且不容易去除。我们分析了两个大的（每个> 27阵列）组织培养实验与广泛的染料交换阵列，以更好地表征染料的偏见。在两个实验中使用间接的氨基-烯丙基标记。我们发现，标准化后的染料偏差，是一致的样品中确实存在许多基因，并控制和纠正这种类型的染料偏差的设计和分析是可取的。这种类型的染料偏见的程度保持不变下广泛的归一化方法（中位数为中心，各种黄土归一化）和统计分析技术（参数，基于秩，基于排列等）。我们还发现染料偏见与个体样本的一个小得多的基因子集。但这些样本特异性染料偏差似乎对细胞系之间的估计基因表达差异影响甚微。
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.