Statistical Methods for Normalizing Microarrays in Cancer Biomarker Studies

癌症生物标志物研究中微阵列标准化的统计方法

• 批准号: 8231280
• 负责人: Li-Xuan Qin
• 金额: $ 40.54万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8231280
• 关键词: Benchmarking; Biological; Biological Markers; Biology; Biometry; Clinical Research; Data; Data Analyses; Data Set; Detection; Development; Disease; Etiology; Evaluation; Experimental Designs; Female Genital Neoplasms; Gene Expression; Genomics; Goals; Gold; Heterogeneity; Interdisciplinary Study; Malignant Neoplasms; Measurement; Methods; MicroRNAs; Microarray Analysis; Modeling; Molecular; Outcome; Pattern; Principal Investigator; Process; Property; Randomized; Sampling; Statistical Methods; Statistical Models; Surgical Oncology; Testing; Time; Tissue Banks; anticancer research; base; design; experience; genome-wide; liposarcoma; mRNA Expression; molecular marker; novel; public health relevance; statistics; tool; tumor

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.

描述（由申请人提供）：已经开发了用于基因表达微阵列的各种阵列归一化方法。大多数这些方法假设样本组之间的差异表达很少或对称。目前还没有对这些方法在利用肿瘤等异质样品标准化 microRNA 表达阵列方面的特性进行系统研究。 MicroRNA 阵列仅包含几百个 microRNA，并且可能有相对较大比例的 microRNA 在不同肿瘤组之间差异表达。由于缺乏没有混杂数组效应的基准数据集，因此在这种情况下评估归一化方法很困难。我们建议设计和生成这样的基准数据集，对标准化方法进行系统评估，特别强调这些模型在检测具有差异表达的标记物方面的实用性，并从基准数据设计中推导出承认肿瘤样本固有异质性的统计模型。 公众健康相关性：微阵列广泛用于癌症研究。处理微阵列数据的关键步骤是对阵列进行归一化，以便不同阵列的测量结果具有可比性。当将阵列标准化统计方法应用于利用肿瘤等异质样品的 microRNA 阵列时，非常需要评估阵列标准化统计方法的特性。