Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling.
Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling.
复制标题
使用序列匹配和回归模型转换各代 Affymetrix 微阵列的表达强度。
DOI：
10.1093/nar/gni159
复制
发表时间：
2005-10-13
影响因子：
14.9
通讯作者：
Mariani, TJ
中科院分区：
文献类型：
--
作者：
Bhattacharya, S;Mariani, TJ
先前生成的微阵列数据的效用由于研究规模小而受到严重限制，导致分析能力不足和复制失败。平台的多样性和系统噪声的各种来源限制了从类似研究中汇编现有数据的能力。我们提出了一个模型，用于跨不同代的Affymble阵列的数据转换，开发使用以前发布的数据集，描述了两代阵列进行的技术复制。该转换基于探针组特定的回归模型，该模型由跨平台的重复测量生成，使用相关系数执行。该模型，当应用于5069个共享的表达强度，在三个不同代的Affytron人寡核苷酸阵列序列匹配的探针集，显示出显着改善之间的代间相关性的样品范围内的平均值和个别的探针集对。通过观察到预测值的各代信号强度之间的欧几里德距离减小，进一步验证了该方法。最后，将该模型应用于独立但相关的数据集导致基于其生物学而不是技术属性的样本聚类得到改进。我们的研究结果表明，这种转换方法是一个有价值的工具，整合来自不同代的阵列的微阵列数据集。
The utility of previously generated microarray data is severely limited owing to small study size, leading to under-powered analysis, and failure of replication. Multiplicity of platforms and various sources of systematic noise limit the ability to compile existing data from similar studies. We present a model for transformation of data across different generations of Affymetrix arrays, developed using previously published datasets describing technical replicates performed with two generations of arrays. The transformation is based upon a probe set-specific regression model, generated from replicate measurements across platforms, performed using correlation coefficients. The model, when applied to the expression intensities of 5069 shared, sequence-matched probe sets in three different generations of Affymetrix Human oligonucleotide arrays, showed significant improvement in inter generation correlations between sample-wide means and individual probe set pairs. The approach was further validated by an observed reduction in Euclidean distance between signal intensities across generations for the predicted values. Finally, application of the model to independent, but related datasets resulted in improved clustering of samples based upon their biological, as opposed to technical, attributes. Our results suggest that this transformation method is a valuable tool for integrating microarray datasets from different generations of arrays.
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影响因子：
14.9
作者：
Baugh, L. R.;Hill, A. A.;Hunter, Craig P.
通讯作者：
Hunter, Craig P.
影响因子：
5.1
作者：
Hennig, L;Menges, M;Gruissem, W
通讯作者：
Gruissem, W
影响因子：
7
作者：
Lyons-Weiler, J;Patel, S;Bhattacharya, S
通讯作者：
Bhattacharya, S
影响因子：
4.6
作者：
Mecham, BH;Wetmore, DZ;Mariani, TJ
通讯作者：
Mariani, TJ
DOI：
10.1073/pnas.1534744100
发表时间：
2003-09-30
影响因子：
11.1
作者：
Mei, R;Hubbell, E;Webster, TA
通讯作者：
Webster, TA