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Method and system for normalization of micro array data based on local normalization of rank-ordered, globally normalized data

机译:基于排序的全局归一化数据的局部归一化的微阵列数据归一化的方法和系统

摘要

A method and system for normalizing two or more molecular array data sets. Input molecular array data sets are separately globally normalized by, for example, dividing the feature-signal magnitudes of each data set by the geometric mean of the feature-signal magnitudes of the data set. The globally normalized feature signal magnitudes within each data set are ranked in ascending order. A numeric function is created that relates feature-signal magnitudes of the data sets. Only a subset of the features, obtained by selecting features that are similarly ranked in the separate feature-signal-magnitude rankings for the data sets, is used to construct the numeric function. The numeric function is smoothed by one of many possible different smoothing procedures. The smoothed numeric function is used to rescale the feature-signal magnitude in one data set to the feature-signal magnitude of another data set, or to normalize the data sets to one another by distributing correction terms amongst the feature-signal magnitudes for a feature in each data set.
机译:一种用于标准化两个或更多个分子阵列数据集的方法和系统。通过例如将每个数据集的特征信号幅度除以该数据集的特征信号幅度的几何平均值,分别对输入分子阵列数据集进行全局标准化。每个数据集内的全局归一化特征信号幅度按升序排列。创建一个与数据集的特征信号幅度相关的数值函数。通过选择在数据集的单独特征信号幅值等级中相似地排名的特征而获得的特征的子集仅用于构造数值函数。通过许多可能的不同平滑过程之一对数字函数进行平滑。平滑数值函数用于将一个数据集中的特征信号幅度重新缩放为另一数据集的特征信号幅度,或通过在特征的特征信号幅度之间分配校正项来将数据集彼此归一化在每个数据集中。

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