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The poor performance of TMM on microRNA-Seq

机译:TMM在microRNA-Seq上的性能较差

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We published our third-party comparison of seven different normalization methods that were previously employed in microarray analysis, and TMM, a method developed by Robinson and Oshlack (2010) for RNA-Seq analysis, in the context of microRNA sequencing (miRNA-Seq). We used various evaluation metrics (MSE, KS statistics, ROC curves, linear regression, and differential expression test similarities) on two independent public miRNA-Seq profiling results that had qPCR results for validation. Based on the results that used relevant R packages at the time of publication, we found (Garmire and Subramaniam 2012) that quantile and lowess normalization worked the best on the two public data sets, whereas the normalization step documented in TMM, at the time of manuscript preparation, performed the worst among all methods compared.
机译:我们发布了先前在微阵列分析中使用的七种不同归一化方法的第三方比较,以及由microRNA测序(miRNA-Seq)的Robinson和Oshlack(2010)为RNA-Seq分析开发的TMM方法。 。我们对具有qPCR结果的两个独立的公共miRNA-Seq分析结果使用了各种评估指标(MSE,KS统计数据,ROC曲线,线性回归和差异表达测试相似性)。根据发布时使用相关R包的结果,我们发现(Garmire和Subramaniam 2012),分位数和最低归一化在两个公共数据集上效果最好,而归一化步骤在TMM中记录在案。相比所有方法,手稿准备工作表现最差。

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