Algorithms for differential splicing detection using exon arrays: a comparative assessment
Algorithms for differential splicing detection using exon arrays: a comparative assessment
复制标题
使用外显子阵列进行差异剪接检测的算法：比较评估
作者：
Karin Zimmermann;Marcel Jentsch;A. Rasche;M. Hummel;U. Leser
差异剪接（DS）的分析对于理解细胞和器官的生理过程至关重要。特别是，已知异常转录与包括癌症在内的多种疾病有关。外显子阵列是研究DS的一种广泛使用的技术。在过去的十年中，已经开发了各种用于从外显子阵列检测DS事件的算法。然而，到目前为止，还没有对最重要的数据特征进行包括敏感性在内的全面的比较评价。为此，我们基于模拟数据创建了多个数据集，以评估七种已发表的方法以及一种新开发的方法KLAS的优缺点。此外，我们对包含RT-PCR验证结果的两个癌症数据集评估了所有方法。我们的研究表明，当整合所有情景和数据集的结果时，ARH是最稳健的方法。然而，特殊情况或需求更倾向于其他方法。根据实验数据，FIRMA具有很高的敏感性，而SplicingCompass、MIDAS和ANOSVA在所有情况下都具有很高的特异性。实验数据显示，ARH、FIRMA、MIDAS和KLAS表现最好。每种方法在敏感性、特异性、对某些数据设置的干扰以及对多个数据集的鲁棒性方面表现出不同的特点。虽然有些方法可以被认为是对所有数据集和场景的普遍良好选择，但其他方法在不同数据集上显示出异构的预测质量。必须仔细选择适当的方法，并牢记明确的研究目标。
The analysis of differential splicing (DS) is crucial for understanding physiological processes in cells and organs. In particular, aberrant transcripts are known to be involved in various diseases including cancer. A widely used technique for studying DS are exon arrays. Over the last decade a variety of algorithms for the detection of DS events from exon arrays has been developed. However, no comprehensive, comparative evaluation including sensitivity to the most important data features has been conducted so far. To this end, we created multiple data sets based on simulated data to assess strengths and weaknesses of seven published methods as well as a newly developed method, KLAS. Additionally, we evaluated all methods on two cancer data sets that comprised RT-PCR validated results. Our studies indicated ARH as the most robust methods when integrating the results over all scenarios and data sets. Nevertheless, special cases or requirements favor other methods. While FIRMA was highly sensitive according to experimental data, SplicingCompass, MIDAS and ANOSVA showed high specificity throughout the scenarios. On experimental data ARH, FIRMA, MIDAS, and KLAS performed best. Each method shows different characteristics regarding sensitivity, specificity, interference to certain data settings and robustness over multiple data sets. While some methods can be considered as generally good choices over all data sets and scenarios, other methods show heterogeneous prediction quality on the different data sets. The adequate method has to be chosen carefully and with a defined study aim in mind.