Normalization of microarray data using a spatial mixed model analysis which includes splines
Normalization of microarray data using a spatial mixed model analysis which includes splines
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DOI：
10.1093/bioinformatics/bth384
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发表时间：
2004-11-22
期刊：
影响因子：
5.8
通讯作者：
Wilson, T
中科院分区：
文献类型：
--
作者：
Baird, D;Johnstone, P;Wilson, T
动机：在一个载玻片上具有数千个基因的微阵列实验以及在任何实验组中使用的多个载玻片代表了具有许多变异来源的大量数据。这种来源的变化内的微阵列实验集的识别是正确破译所需的基因表达differences.Results的关键：我们描述了新的方法，使用空间混合模型，其中包括样条和分析的双色斑点内的幻灯片的变化和一系列的幻灯片的微阵列的标准化。该模型通常可以解释载玻片上45-85%的变化，而总自由度仅为1%。从我们的方法的结果相比，从强度依赖的归一化黄土方法，我们占了两倍的幻灯片上的不受控制和不必要的变化。我们还为每个EST开发了一个索引，将差异反应的各种测量值结合到一个单一的值中，研究人员可以使用该值快速评估感兴趣的基因。
Motivation: Microarray experiments with thousands of genes on a slide and multiple slides used in any experimental set represent a large body of data with many sources of variation. The identification of such sources of variation within microarray experimental sets is critical for correct deciphering of desired gene expression differences.Results: We describe new methods for the normalization using spatial mixed models which include splines and analysis of two-colour spotted microarrays for within slide variation and for a series of slides. The model typically explains 45-85% of the variation on a slide with only similar to1% of the total degrees of freedom. The results from our methods compare favourably with those from intensity dependent normalization loess methods where we accounted for twice as much uncontrolled and unwanted variation on the slides. We have also developed an index for each EST that combines the various measures of the differential response into a single value that researchers can use to rapidly assess the genes of interest.