Sub-array normalization subject to differentiation.
Sub-array normalization subject to differentiation.
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DOI：
10.1093/nar/gki844
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发表时间：
2005
影响因子：
14.9
通讯作者：
Li LM
中科院分区：
文献类型：
--
作者：
Cheng C;Li LM
从微阵列测量中，我们寻求不同生物样品之间mRNA表达的差异。然而，由于不受控制的变化，每个阵列都具有“块效应”。减少块效应的统计处理通常被称为归一化。我们的观点是找到一个转换，匹配的阵列之间的未分化的基因对应的那些探针的杂交水平的分布。我们讨论两个重要问题。首先，由于不均匀的杂交和测量过程，存在阵列特定的空间模式。第二，在某些情况下，相当大部分的基因在靶阵列和参考阵列之间差异表达。为了标准化的目的，我们需要确定一个子集，排除那些探针对应的差异表达的基因和异常探针由于实验变异。最小二乘（LTS）是实现这一目标的自然选择。通过设定适当的修剪分数，在LTS中保护实质性分化。为了考虑杂交的任何空间模式，我们将每个阵列分成子阵列并将每个子阵列内的探针强度归一化。我们说明了问题和解决方案，通过一个Affyspike-in数据集与定义的扰动和灵长类动物的大脑表达的数据集。
From microarray measurement, we seek differentiation of mRNA expressions among different biological samples. However, each array has a ‘block effect’ due to uncontrolled variation. The statistical treatment of reducing the block effect is usually referred to as normalization. Our perspective is to find a transformation that matches the distributions of hybridization levels of those probes corresponding to undifferentiated genes between arrays. We address two important issues. First, array-specific spatial patterns exist due to uneven hybridization and measurement process. Second, in some cases a substantially large portion of genes are differentially expressed between a target and a reference array. For the purpose of normalization we need to identify a subset that exclude those probes corresponding to differentially expressed genes and abnormal probes due to experimental variation. Least trimmed squares (LTS) is a natural choice to achieve this goal. Substantial differentiation is protected in LTS by setting an appropriate trimming fraction. To take into account any spatial pattern of hybridization, we divide each array into sub-arrays and normalize probe intensities within each sub-array. We illustrate the problem and solution through an Affymetrix spike-in dataset with defined perturbation and a dataset of primate brain expression.
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影响因子：
7.7
作者：
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通讯作者：
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影响因子：
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通讯作者：
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