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: 8453253
• 负责人: Li-Xuan Qin
• 金额: $ 28.77万
• 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/8453253
• 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 Chips; 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.

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。这些方法大多数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，特别强调这些模型用于检测差异表达标记物的效用，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。