Oligonucleotide microarray data distribution and normalization
Oligonucleotide microarray data distribution and normalization
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DOI：
10.1016/s0020-0255(02)00215-3
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发表时间：
2002-10-01
影响因子：
8.1
通讯作者：
Dimitrov, DS
中科院分区：
文献类型：
--
作者：
Sidorov, IA;Hosack, DA;Dimitrov, DS
由于各种因素（包括样品浓度的差异）导致的寡核苷酸微阵列探针信号的变化，可能导致解释从不同实验中获得的数据的主要问题。这种信号的归一化通常是通过这样的过程来完成的，包括除以一个由平均信号强度近似决定的常数，例如在Affymetrix软件中。在这里，我们证明了Affymetrix寡核苷酸探针信号分布可以通过使用两个正态分布或两个极值分布的叠加来拟合，并且通过使用这些分布我们可以高精度地规范化数据（参数算法）。我们还开发了基于信号强度排序的第二种算法（非参数），该算法的归一化效果与参数算法相同或更好。这些方法已用于三组数据的归一化，这些数据分别来自癌细胞系、HIV感染患者的外周血单个核细胞和糖尿病患者的脂肪细胞等。与标准的全局归一化方法[Affymetrix微阵列套件用户指南]相比，参数化和非参数化归一化程序都被发现是优越的。版本4.0(2000)]。这些结果表明，新方法可能有助于微阵列数据规范化，特别是在患者间差异可能很大且难以避免的临床数据比较中。(C) 2002年Elsevier Science Inc.出版
Variations in oligonucleotide microarray probe signals that result from various factors, including differences in sample concentrations, can lead to major problems in the interpretation of data obtained from different experiments. Normalization of such signals is typically performed by procedures involving division by a constant approximately determined by average signal intensities as, e.g., in the Affymetrix software. Here we show that Affymetrix oligonucleotide probe signal distributions can be fitted by using a superposition of two normal or two extreme distributions, and that by using such distributions we can normalize data with high accuracy (parametric algorithm). We also developed a second algorithm (nonparametric) based on ranking of signal intensities which gave equal or better normalization than the parametric one. These approaches have been used for normalization of three sets of data obtained from cancer cell lines, peripheral blood mononuclear cells from patients with HIV infections, and adipose cells from patients with diabetes, and others. Both, parametric and nonparametric normalization procedures, were found to be superior when compared to the standard global normalization approach [Affymetrix Microarray Suite User Guide. Version 4.0 (2000)]. These results suggest that the new approaches may be helpful for microarray data normalization especially for comparison of clinical data where interpatient differences can be large and difficult to avoid. (C) 2002 Published by Elsevier Science Inc.