Schmidt Marcin T, Handschuh Luiza, Zyprych Joanna, Szabelska Alicja, Olejnik-Schmidt Agnieszka K, Siatkowski Idzi, Figlerowicz Marek
Department of Biotechnology and Food Microbiology, Poznan University of Life Sciences, Poznań, Poland.
Acta Biochim Pol. 2011;58(4):573-80. Epub 2011 Dec 20.
Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.
双色DNA微阵列常用于全局基因表达分析。它们提供了数千种mRNA相对丰度的信息。然而，生成的数据需要进行归一化处理，以尽量减少系统变异，从而更易于识别生物学上的显著差异。已经提出了大量的归一化程序，并且有许多用于微阵列数据分析的软件。在这里，我们应用了来自两个微阵列数据分析软件包的两种归一化方法（中位数法和局部加权回归法）。使用一个样本数据集对它们进行了检验。我们发现，根据所应用的方法，被鉴定为差异表达的基因数量有显著差异。只有当我们使用中位数归一化方法时，所获得的结果，即差异表达基因列表，才是一致的。两个软件包中实现的局部加权回归归一化提供的一致性较差，甚至对某些探针产生了相互矛盾的结果。总体而言，我们的结果提供了额外的证据，表明归一化方法会深刻影响基于DNA微阵列分析的最终结果。归一化方法的影响在很大程度上取决于所采用的算法。因此，对于每个单独的数据集，必须仔细考虑并优化归一化程序。
