rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data
rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data
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DOI：
10.1073/pnas.1419161111
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发表时间：
2014-12-23
影响因子：
11.1
通讯作者：
Xing, Yi
中科院分区：
文献类型：
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作者：
Shen, Shihao;Park, Juw Won;Xing, Yi
超深度RNA测序（RNA-Seq）已成为全基因组分析前体mRNA选择性剪接的有力方法。我们以前开发了转录剪接的多变量分析（MATS），这是一种用于检测两个RNA-Seq样本之间差异选择性剪接的统计方法。在这里，我们描述了一个新的统计模型和计算机程序，重复MATS（rMATS），设计用于检测差异选择性剪接从重复RNA-Seq数据。rMATS使用分层模型同时考虑单个重复样本的采样不确定性和重复样本之间的变异性。除了分析未配对的重复样本外，rMATS还包括专为样本组之间的配对重复样本设计的模型。rMATS的假设检验框架是灵活的，并且可以评估任何用户定义的剪接变化幅度的统计学显著性。通过分析模拟和真实的RNA-Seq数据来评估rMATS的性能。rMATS在所有模拟环境中的重复RNA-Seq数据的表现优于两种现有方法，RT-PCR在前列腺癌细胞系的RNA-Seq数据集中产生了高验证率（94%）。我们的数据还为设计选择性剪接的RNA-Seq研究提供了指导原则。我们证明，在研究设计中纳入生物重复是至关重要的。值得注意的是，合并RNA或合并来自多个重复的RNA-Seq数据不是解释变异性的有效方法，并且结果对离群值特别敏感。rMATS源代码可在rnaseq-mats.sourceforge.net/上免费获得。随着RNA-Seq的普及，我们预计rMATS将有助于在不同的RNA-Seq项目中研究选择性剪接。
Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.