Adjustments and measures of differential expression for microarray data
Adjustments and measures of differential expression for microarray data
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DOI：
10.1093/bioinformatics/18.2.251
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发表时间：
2002-02-01
期刊：
影响因子：
5.8
通讯作者：
Jones, D
中科院分区：
文献类型：
--
作者：
Tsodikov, A;Szabo, A;Jones, D
动机：现有的微阵列数据分析通常包括在数据分析之前应用的模糊数据归一化程序。例如，对微阵列通道强度的比率进行归一化，以使其在该组基因上具有共同平均值。我们试图从建模的角度理解这些过程的含义，并阐明支撑它们的模型假设。结果：提出了一种两步统计程序：先进行数据转换(针对玻片特有效应进行调整)，然后对转换后的数据进行统计检验。使用模拟和结肠癌细胞系的真实数据，比较了差异表达的各种分析方法。我们发现，稳健的分类调整优于基于精确定义的随机模型的调整，包括一些常用的程序。
Motivation: Existing analyses of microarray data often incorporate an obscure data normalization procedure applied prior to data analysis. For example, ratios of microarray channels intensities are normalized to have common mean over the set of genes. We made an attempt to understand the meaning of such procedures from the modeling point of view, and to formulate the model assumptions that underlie them. Given a considerable diversity of data adjustment procedures, the question of their performance, comparison and ranking for various microarray experiments was of interest.Results: A two-step statistical procedure is proposed: data transformation (adjustment for slide-specific effect) followed by a statistical test applied to transformed data. Various methods of analysis for differential expression are compared using simulations and real data on colon cancer cell lines. We found that robust categorical adjustments outperform the ones based on a precisely defined stochastic model, including some commonly used procedures.