Statistical Methods for Normalizing Microarrays in Cancer Biomarker Studies

描述(由申请人提供)：已经为基因表达微阵列开发了各种阵列归一化方法。这些方法大多假定样本组之间的差异表达很少或对称。在利用不同种类的样本如肿瘤使microRNA表达阵列归一化方面，还没有对这些方法的特性进行系统的研究。MicroRNA阵列只包含几百个microRNA，并且可能有相对较大的比例在不同的肿瘤组之间差异表达。在这种情况下对归一化方法的评估是困难的，因为缺乏不具有混杂数组效果的基准数据集。我们建议设计和生成这样的基准数据集，对归一化方法进行系统的评估，特别强调这些模型对于检测差异表达标记的实用性，并从基准数据设计中得出承认肿瘤样本固有的异质性的统计模型。 与公共健康相关：微阵列正被广泛应用于癌症研究。处理微阵列数据的一个关键步骤是将阵列归一化，以便不同阵列的测量结果具有可比性。当将用于阵列归一化的统计方法应用于利用诸如肿瘤等异质样本的microRNA阵列时，非常需要评估这些统计方法的性能。

DESCRIPTION (provided by applicant): Various array normalization methods have been developed for gene expression microarrays. Most of these methods assume few or symmetric differential expression between sample groups. There has been no systematic study of the properties of these methods in normalizing microRNA expression arrays utilizing heterogeneous samples such as tumors. MicroRNA arrays contain only a few hundred microRNAs, and are likely to have a relatively large proportion being differentially expressed between diverse tumor groups. The assessment of normalization methods in this setting is difficult because of the lack of a benchmark dataset that has no confounding array effects. We propose to design and generate such benchmark datasets, perform a systematic assessment of normalization methods with a particular emphasis on the utility of these models for detecting markers with differential expression, and from the benchmark data design derive statistical models that acknowledge heterogeneities inherent to tumor samples. PUBLIC HEALTH RELEVANCE: Microarrays are being widely used in cancer research. A critical step for processing microarray data is to normalize the arrays so that measurements from different arrays are comparable. There is a great need to evaluate the properties of statistical methods for array normalization when they are applied to microRNA arrays utilizing heterogeneous samples such as tumors.