Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization.
Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization.
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DOI：
10.1186/1471-2105-11-156
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发表时间：
2010-03-26
影响因子：
3
通讯作者：
Reinders MJ
中科院分区：
文献类型：
--
作者：
Hulsman M;Mentink A;van Someren EP;Dechering KJ;de Boer J;Reinders MJ
寡核苷酸阵列已成为生物学中最广泛使用的高通量工具之一。由于它们对实验条件的敏感性，在比较这些阵列的测量结果时，归一化是关键的一步。然而，正常化远不是一个解决的问题。我们经常会遇到具有重大技术影响的数据集，而目前可用的方法无法纠正这些影响。我们表明，通过仔细分解的探针特异性扩增，杂交和阵列位置的影响，可以进行归一化，使这些数据的分析得到很大的改善。阵列之间的变化的技术来源的识别，使我们能够建立统计模型，用于估计如何影响单个探针的信号，基于它们的属性。这使得基于模型的归一化能够是探针特异性的，与许多当前方法执行的信号强度分布归一化形成对比。接下来，我们提出了一种新的处理背景校正的方法，使背景信息的权重探测在总结过程中。所提出的方法的测试表明，差异表达基因的检测比早期提出的方法有很大的改进，即使在（实验严格控制和复制）加标数据集上进行测试。当有限数量的阵列可用时，或者当阵列以不同批次运行时，技术效果对所测量的基因表达有很大影响。我们表明，这些技术效果的详细建模和校正允许在这些情况下改进分析。
Oligonucleotide arrays have become one of the most widely used high-throughput tools in biology. Due to their sensitivity to experimental conditions, normalization is a crucial step when comparing measurements from these arrays. Normalization is, however, far from a solved problem. Frequently, we encounter datasets with significant technical effects that currently available methods are not able to correct. We show that by a careful decomposition of probe specific amplification, hybridization and array location effects, a normalization can be performed that allows for a much improved analysis of these data. Identification of the technical sources of variation between arrays has allowed us to build statistical models that are used to estimate how the signal of individual probes is affected, based on their properties. This enables a model-based normalization that is probe-specific, in contrast with the signal intensity distribution normalization performed by many current methods. Next to this, we propose a novel way of handling background correction, enabling the use of background information to weight probes during summarization. Testing of the proposed method shows a much improved detection of differentially expressed genes over earlier proposed methods, even when tested on (experimentally tightly controlled and replicated) spike-in datasets. When a limited number of arrays are available, or when arrays are run in different batches, technical effects have a large influence on the measured expression of genes. We show that a detailed modelling and correction of these technical effects allows for an improved analysis in these situations.
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影响因子：
14.9
作者：
Skvortsov D;Abdueva D;Curtis C;Schaub B;Tavaré S
通讯作者：
Tavaré S
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
--
作者：
Both, Sanne K.;Van der Muijsenberg, Adrie J. C.;De Bruijn, Joost D.
通讯作者：
De Bruijn, Joost D.
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
影响因子：
14.9
作者：
Cheng C;Li LM
通讯作者：
Li LM