Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles.
Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles.
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纠正 Illumina BeadChip 数据的实验内变异对于生成可靠的基因表达谱是必要的。
DOI：
10.1186/1471-2164-11-134
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发表时间：
2010-02-24
期刊：
影响因子：
4.4
通讯作者：
Bartlett JM
中科院分区：
文献类型：
--
作者：
Kitchen RR;Sabine VS;Sims AH;Macaskill EJ;Renshaw L;Thomas JS;van Hemert JI;Dixon JM;Bartlett JM
微阵列技术是制作全基因组转录谱的常用手段，然而mRNA的高成本和稀缺性导致许多研究都是基于单样本分析进行的。我们利用Illumina平台的设计，特别是每个芯片上的多个阵列，利用通用人类参考RNA （UHRR）的重复杂交和来自临床研究的原发性乳腺肿瘤样本的重复杂交来评估实验内的技术变化。在UHRR和临床样本的测量表达中检测到明显的批次特异性偏倚。这种偏差被发现在采用标准微阵列归一化技术后仍然存在。然而，当对数据应用均值居中或经验贝叶斯批次校正方法（ComBat）时，UHRR和临床样本的批次间差异大大降低。使用ComBat进行批量校正后，重复UHRR样本之间的相关性提高了两个数量级（范围从0.9833-0.9991到0.9997-0.9999），并增加了重复临床样本基因列表的一致性，从分位数归一化数据的11.6%增加到批量校正数据的66.4%。使用UHRR作为批间校准器与ComBat结合使用时提供了一个小的额外好处，进一步增加了两个基因列表之间的一致性，高达74.1%。考虑到实用性和成本，这些结果表明，单样本可以产生可靠的数据，但只有在仔细补偿实验中的技术偏差之后。我们建议研究者在微阵列实验的设计阶段认识到这种变化的倾向，并在数据统计分析过程中使用适当的校正方法。
Microarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study. A clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%. In the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data.
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影响因子：
2.7
作者：
Sims, Andrew H.;Smethurst, Graeme J.;Hey, Yvonne;Okoniewski, Michal J.;Pepper, Stuart D.;Howell, Anthony;Miller, Crispin J.;Clarke, Robert B.
通讯作者：
Clarke, Robert B.
影响因子：
7
作者：
Kuhn, K;Baker, SC;Chee, MS
通讯作者：
Chee, MS
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
78.5
作者：
Ransohoff, DF
通讯作者：
Ransohoff, DF
影响因子：
5.8
作者：
Benito, M;Parker, J;Marron, JS
通讯作者：
Marron, JS