Evaluation of normalization methods in mammalian microRNA-Seq data
Evaluation of normalization methods in mammalian microRNA-Seq data
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DOI：
10.1261/rna.030916.111
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发表时间：
2012-06-01
期刊：
影响因子：
4.5
通讯作者：
Subramaniam, Shankar
中科院分区：
文献类型：
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作者：
Garmire, Lana Xia;Subramaniam, Shankar
简单的总标签计数标准化不足以满足下一代测序技术生成的 microRNA 测序数据。然而，迄今为止，缺乏对 microRNA 测序数据标准化方法的系统评估。我们综合评估了七种常用的归一化方法，包括全局归一化、Lowess归一化、截尾均值法（TMM）、分位数归一化、尺度归一化、方差稳定化和不变法。我们使用均方误差 (MSE) 和 Kolmogorov-Smirnov (K-S) 统计量的经验统计指标在两个单独的实验数据集上评估这些方法。此外，我们还利用定量 PCR 验证的结果来评估这些方法。我们的结果一致表明，Lowess 标准化和分位数标准化表现最好，而 TMM（一种应用于 RNA 测序标准化的方法）表现最差。 microRNA-Seq 数据差异表达 (DE) 测试的异常结果进一步证明了 TMM 归一化性能不佳。与DE所使用的模型相比，归一化方法的选择是影响DE结果的首要因素。综上所述，推荐使用Lowess归一化和分位数归一化来标准化microRNA-Seq数据，而应谨慎使用TMM方法。
Simple total tag count normalization is inadequate for microRNA sequencing data generated from the next generation sequencing technology. However, so far systematic evaluation of normalization methods on microRNA sequencing data is lacking. We comprehensively evaluate seven commonly used normalization methods including global normalization, Lowess normalization, Trimmed Mean Method (TMM), quantile normalization, scaling normalization, variance stabilization, and invariant method. We assess these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic. Additionally, we evaluate the methods with results from quantitative PCR validation. Our results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst. The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.