Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments.
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments.
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DOI：
10.1186/1471-2105-11-94
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发表时间：
2010-02-18
影响因子：
3
通讯作者：
Dudoit S
中科院分区：
文献类型：
--
作者：
Bullard JH;Purdom E;Hansen KD;Dudoit S
高通量测序技术，如Illumina基因组分析仪，是研究广泛的生物和医学问题的强大新工具。统计和计算方法是从测序仪生成的大量复杂数据集中得出有意义和准确结论的关键。我们对Illumina转录组测序（mRNA-Seq）数据的标准化和差异表达（DE）分析的统计方法进行了详细评估。我们比较了两种类型的生物样本之间的显着DE检测基因的统计方法，发现有很大的差异，在如何处理低计数基因的测试统计。我们评估了DE结果如何受到测序平台特征的影响，例如，不同的基因长度、碱基调用校准方法（有和没有phi X对照泳道）以及流动池/文库制备效应。我们研究了读段计数归一化方法对DE结果的影响，并表明通过总泳道计数进行缩放的标准方法（例如，RPKM）可使DE的估计值产生偏倚。我们提出了更一般的基于分位数的归一化程序，并证明了DE检测的改进。我们的研究结果对mRNA-Seq实验的设计和分析具有重要的实践和方法学意义。他们强调了标准化和DE推断的适当统计方法的重要性，以解释可能影响结果准确性的测序平台特征。他们还揭示了需要进一步研究mRNA-Seq的统计和计算方法的发展。
High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data. We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection. Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.
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影响因子：
46.9
作者：
Canales, Roger D.;Luo, Yuling;Goodsaid, Federico M.
通讯作者：
Goodsaid, Federico M.
影响因子：
14.9
作者：
't Hoen PA;Ariyurek Y;Thygesen HH;Vreugdenhil E;Vossen RH;de Menezes RX;Boer JM;van Ommen GJ;den Dunnen JT
通讯作者：
den Dunnen JT
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
影响因子：
12.3
作者：
Langmead B;Trapnell C;Pop M;Salzberg SL
通讯作者：
Salzberg SL
影响因子：
48
作者：
Mortazavi, Ali;Williams, Brian A.;Wold, Barbara
通讯作者：
Wold, Barbara