Department for Research, Forerunner Pharma Research, Co,, Ltd, Yokohama, Japan.
BMC Bioinformatics. 2014 Jun 30;15:228. doi: 10.1186/1471-2105-15-228.
Knockdown or overexpression of genes is widely used to identify genes that play important roles in many aspects of cellular functions and phenotypes. Because next-generation sequencing generates high-throughput data that allow us to detect genes, it is important to identify genes that drive functional and phenotypic changes of cells. However, conventional methods rely heavily on the assumption of normality and they often give incorrect results when the assumption is not true. To relax the Gaussian assumption in causal inference, we introduce the non-paranormal method to test conditional independence in the PC-algorithm. Then, we present the non-paranormal intervention-calculus when the directed acyclic graph (DAG) is absent (NPN-IDA), which incorporates the cumulative nature of effects through a cascaded pathway via causal inference for ranking causal genes against a phenotype with the non-paranormal method for estimating DAGs.
We demonstrate that causal inference with the non-paranormal method significantly improves the performance in estimating DAGs on synthetic data in comparison with the original PC-algorithm. Moreover, we show that NPN-IDA outperforms the conventional methods in exploring regulators of the flowering time in Arabidopsis thaliana and regulators that control the browning of white adipocytes in mice. Our results show that performance improvement in estimating DAGs contributes to an accurate estimation of causal effects.
Although the simplest alternative procedure was used, our proposed method enables us to design efficient intervention experiments and can be applied to a wide range of research purposes, including drug discovery, because of its generality.
敲低或过表达基因被广泛用于鉴定在细胞功能和表型的许多方面发挥重要作用的基因。由于下一代测序产生了允许我们检测基因的高通量数据，因此识别驱动细胞功能和表型变化的基因非常重要。然而，传统方法严重依赖正态性假设，当假设不成立时，它们经常给出不正确的结果。为了放宽因果推断中的正态性假设，我们在 PC 算法中引入非正态方法来检验条件独立性。然后，当有向无环图 (DAG) 不存在时，我们提出了非正态干预微积分 (NPN-IDA)，它通过因果推断整合了效应的累积性质，通过级联途径对因果基因进行排名，以针对表型进行非正态方法估计 DAG。
我们证明，与原始 PC 算法相比，使用非正态方法进行因果推断可以显著提高在合成数据上估计 DAG 的性能。此外，我们表明，NPN-IDA 在探索拟南芥开花时间的调节剂和控制小鼠白色脂肪细胞褐变的调节剂方面优于传统方法。我们的结果表明，估计 DAG 的性能改进有助于对因果效应的准确估计。
尽管使用了最简单的替代程序，但由于其通用性，我们提出的方法使我们能够设计有效的干预实验，并可应用于广泛的研究目的，包括药物发现。
