Hu Jianhua, He Xuming
Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, USA.
Biometrics. 2007 Mar;63(1):50-9. doi: 10.1111/j.1541-0420.2006.00670.x.
In microarray experiments, removal of systematic variations resulting from array preparation or sample hybridization conditions is crucial to ensure sensible results from the ensuing data analysis. For example, quantile normalization is routinely used in the treatment of both oligonucleotide and cDNA microarray data, even though there might be some loss of information in the normalization process. We recognize that the ideal normalization, if it ever exists, would aim to keep the maximal amount of gene profile information with the lowest possible noise. With this objective in mind, we propose a valuable enhancement to quantile normalization, and demonstrate through three Affymetrix experiments that the enhanced normalization can result in better performance in detecting and ranking differentially expressed genes across experimental conditions.
在微阵列实验中，消除由阵列制备或样品杂交条件导致的系统变异对于确保后续数据分析得出合理结果至关重要。例如，分位数标准化通常用于处理寡核苷酸和cDNA微阵列数据，尽管在标准化过程中可能会有一些信息损失。我们认识到，理想的标准化（如果存在的话）旨在以尽可能低的噪声保留最大量的基因谱信息。出于这一目标，我们提出了对分位数标准化的一项有价值的改进，并通过三项Affymetrix实验证明，改进后的标准化在检测和排列不同实验条件下的差异表达基因方面能够带来更好的性能。
