Chua Su-Wen, Vijayakumar Praveen, Nissom Peter M, Yam Chew-Yeam, Wong Victor V T, Yang He
Bioinformatics Institute #07-01, Matrix, 30 Biopolis Street, Singapore 138671.
Nucleic Acids Res. 2006 Mar 9;34(5):e38. doi: 10.1093/nar/gkl024. Print 2006.
Normalization of cDNA and oligonucleotide microarray data has become a standard procedure to offset non-biological differences between two samples for accurate identification of differentially expressed genes. Although there are many normalization techniques available, their ability to accurately remove systematic variation has not been sufficiently evaluated. In this study, we performed experimental validation of various normalization methods in order to assess their ability to accurately offset non-biological differences (systematic variation). The limitations of many existing normalization methods become apparent when there are unbalanced shifts in transcript levels. To overcome this limitation, we have proposed a novel normalization method that uses a matching algorithm for the distribution peaks of the expression log ratio. The robustness and effectiveness of this method was evaluated using both experimental and simulated data.
对互补DNA（cDNA）和寡核苷酸微阵列数据进行归一化已成为一种标准程序，用于抵消两个样本之间的非生物学差异，以便准确识别差异表达基因。尽管有许多可用的归一化技术，但它们准确消除系统变异的能力尚未得到充分评估。在本研究中，我们对各种归一化方法进行了实验验证，以评估它们准确抵消非生物学差异（系统变异）的能力。当转录水平存在不平衡变化时，许多现有归一化方法的局限性就会变得明显。为克服这一局限性，我们提出了一种新颖的归一化方法，该方法使用一种针对表达对数比值分布峰的匹配算法。使用实验数据和模拟数据对该方法的稳健性和有效性进行了评估。
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