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）：为基因表达微阵列开发了各种阵列归一化方法。这些方法中的大多数假设样品组之间的差异表达很少或对称差分表达。尚无对这些方法的特性进行系统的研究，该特性利用肿瘤等异质样品（例如肿瘤）标准化microRNA表达阵列。 microRNA阵列仅包含几百个microRNA，并且在不同的肿瘤组之间可能具有相对较大的比例。由于缺乏没有混淆阵列效应的基准数据集，因此在这种情况下对归一化方法的评估很困难。我们建议设计和生成此类基准数据集，对归一化方法进行系统评估，并特别强调这些模型用于检测具有差异表达的标记的实用性，并从基准测试数据设计得出统计模型，这些模型得出了确认肿瘤样品固有的异质性的统计模型。 公共卫生相关性：微阵列广泛用于癌症研究中。处理微阵列数据的关键步骤是使阵列归一化，以使来自不同阵列的测量值可比。当使用异质样品（如肿瘤）应用于microRNA阵列时，非常需要评估统计方法的统计方法。