Shrinkage-based similarity metric for cluster analysis of microarray data
Shrinkage-based similarity metric for cluster analysis of microarray data
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DOI：
10.1073/pnas.1633770100
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发表时间：
2003-08-19
影响因子：
11.1
通讯作者：
Mishra, B
中科院分区：
文献类型：
--
作者：
Cherepinsky, V;Feng, JW;Mishra, B
目前用于微阵列数据分析的标准相关系数是由M.B.Eisen、P.T.Spellman、P.O.Brown和D.Botstein[(1998)Proc.纳蒂。阿卡德科学公司。美国95,1486314868]。它的提法相当武断。基于James-Stein收缩估计，我们给出了两个数据向量的严格相关系数。我们使用了Eisen等人描述的假设，也使用了数据可以被视为转换为正态分布的事实。虽然Eisen et A使用零作为表达载体均值Mu的估计器，但我们从假设对于每个基因，IL本身是零均值正态随机变量[具有先验分布N(0，tau(2))]开始，并使用贝叶斯分析来获得关于数据的Mu的后验分布。Mu的缩小估计器不同于数据向量的平均值，并最终导致相关系数的统计稳健估计器。为了评估收缩的有效性，我们在电子实验中进行了研究，并使用Eisen et A的数据集对生物实例的相似性度量进行了比较，对于后者，我们通过基于相关系数的各种定义计算聚类并将其与基于文献中已知的激活剂的聚类进行比较，对参与酵母细胞周期功能调节的基因进行了分类。这项研究估计的假阳性和假阴性表明，使用收缩度量提高了分析的准确性。
The current standard correlation coefficient used in the analysis of microarray data was introduced by M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein [(1998) Proc. Nati. Acad Sci. USA 95, 1486314868]. Its formulation is rather arbitrary. We give a mathematically rigorous correlation coefficient of two data vectors based on James-Stein shrinkage estimators. We use the assumptions described by Eisen et al., also using the fact that the data can be treated as transformed into normal distributions. While Eisen et A use zero as an estimator for the expression vector mean mu, we start with the assumption that for each gene, IL is itself a zero-mean normal random variable [with a priori distribution N(0, tau(2))], and use Bayesian analysis to obtain a posteriori distribution of mu in terms of the data. The shrunk estimator for mu differs from the mean of the data vectors and ultimately leads to a statistically robust estimator for correlation coefficients. To evaluate the effectiveness of shrinkage, we conducted in silico experiments and also compared similarity metrics on a biological example by using the data set from Eisen et A For the latter, we classified genes involved in the regulation of yeast cell-cycle functions by computing clusters based on various definitions of correlation coefficients and contrasting them against clusters based on the activators known in the literature. The estimated false positives and false negatives from this study indicate that using the shrinkage metric improves the accuracy of the analysis.