Chen Dung-Tsa, Lin Sue-Hwa, Soong Seng-Jaw
Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, University of Alabama at Birmingham, 153 Wallace Tumor Institute, 1824 6th Avenue South, Birmingham, AL 35294, USA.
Bioinformatics. 2004 Apr 12;20(6):854-62. doi: 10.1093/bioinformatics/btg493. Epub 2004 Jan 29.
Analysis of oligonucleotide array data, especially to select genes of interest, is a highly challenging task because of the large volume of information and various experimental factors. Moreover, interaction effect (i.e. expression changes depend on probe effects) complicates the analysis because current methods often use an additive model to analyze data. We propose an approach to address these issues with the aim of producing a more reliable selection of differentially expressed genes. The approach uses the rank for normalization, employs the percentile-range to measure expression variation, and applies various filters to monitor expression changes.
We compare our approach with MAS and Dchip models. A data set from an angiogenesis study is used for illustration. Results show that our approach performs better than other methods either in identification of the positive control gene or in PCR confirmatory tests. In addition, the invariant set of genes in our approach provides an efficient way for normalization.
由于信息量巨大以及各种实验因素，分析寡核苷酸阵列数据，尤其是选择感兴趣的基因，是一项极具挑战性的任务。此外，交互效应（即表达变化取决于探针效应）使分析变得复杂，因为当前方法通常使用加法模型来分析数据。我们提出一种方法来解决这些问题，目的是更可靠地选择差异表达基因。该方法使用秩进行归一化，采用百分位数范围来衡量表达变化，并应用各种过滤器来监测表达变化。
我们将我们的方法与MAS和Dchip模型进行了比较。使用来自一项血管生成研究的数据集进行说明。结果表明，我们的方法在识别阳性对照基因或PCR验证测试中均比其他方法表现更好。此外，我们方法中的不变基因集为归一化提供了一种有效方式。
