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Sample Size Calculation Through the Incorporation of Heteroscedasticity and Dependence for a Penalized t-Statistic in Microarray Experiments

机译:通过结合杂乱性和微阵列实验中受罚t统计量的依赖性进行样本量计算

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摘要

When identifying the differentially expressed genes (DEGs) in microarray data, we often observe heteroscedasticity between groups and dependence among genes. Incorporating these factors is necessary for sample size calculation in microarray experiments. A penalized t-statistic is widely used to improve the identifiability of DEGs. We develop a formula to calculate sample size with dependence adjustment for the penalized t-statistic. Sample size is determined on the basis of overall power under certain conditions to maintain a certain false discovery rate. The usefulness of the proposed method is demonstrated by numerical studies using both simulated data and real data.View full textDownload full textKey WordsFalse discovery rate, Gene expression, Microarray, Sample sizeRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10543406.2010.528820
机译:在鉴定微阵列数据中的差异表达基因(DEG)时,我们经常观察到群体之间的异方差性以及基因之间的依赖性。纳入这些因素对于微阵列实验中样本量的计算是必要的。惩罚性t统计量被广泛用于提高DEG的可识别性。我们开发了一个公式来计算受罚t统计量的依存关系调整的样本量。样本大小是在一定条件下根据总功率确定的,以保持一定的错误发现率。通过使用模拟数据和真实数据进行数值研究,证明了该方法的有效性。 “ citeulike,netvibes,twitter,technorati,美味,linkedin,facebook,stumbleupon,digg,google,更多”,pubid:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10543406.2010.528820

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