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Sample sizes for a robust ranking and selection of genes in microarray experiments.

机译:用于微阵列实验中基因的稳健排名和选择的样本量。

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摘要

The main role of high-throughput microarrays today is screening of relevant genes from a large pool of candidate genes. For prioritizing genes for subsequent studies, gene ranking based on the strength of the association with the phenotype is a relevant statistical output. In this article, we propose sample size calculations based on gene ranking and selection using the non-parametric Mann-Whitney-Wilcoxon statistic in microarray experiments. The use of the non-parametric statistic is expected to be advantageous in robustification in gene ranking for the deviation from normality and for possible scale change by using different platforms such as polymerase chain reaction-based platforms in subsequent studies in gene expression data. Application to the data set from a clinical study for lymphoma is given.
机译:今天,高通量微阵列的主要作用是从大量候选基因中筛选相关基因。为了对基因进行优先排序以进行后续研究,基于与表型的关联强度进行基因排名是相关的统计输出。在本文中,我们提出了使用微阵列实验中的非参数Mann-Whitney-Wilcoxon统计量基于基因排名和选择进行样本量计算。通过在随后的基因表达数据研究中使用不同的平台(例如基于聚合酶链反应的平台),使用非参数统计量有望在基因排序的稳健性方面获得优势,以偏离正常值并可能改变规模。给出了针对淋巴瘤临床研究的数据集的应用。

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