Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA.
Bioinformatics. 2012 Apr 15;28(8):1114-21. doi: 10.1093/bioinformatics/bts090. Epub 2012 Feb 21.
The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges.
We integrate available biological knowledge by constructing a network of molecular interactions of a specific kind: causal interactions. The resulting causal graph can be queried to suggest molecular hypotheses that explain the variations observed in a high-throughput gene expression experiment. We show that a simple scoring function can discriminate between a large number of competing molecular hypotheses about the upstream cause of the changes observed in a gene expression profile. We then develop an analytical method for computing the statistical significance of each score. This analytical method also helps assess the effects of random or adversarial noise on the predictive power of our model.
Our results show that the causal graph we constructed from known biological literature is extremely robust to random noise and to missing or spurious information. We demonstrate the power of our causal reasoning model on two specific examples, one from a cancer dataset and the other from a cardiac hypertrophy experiment. We conclude that causal reasoning models provide a valuable addition to the biologist's toolkit for the interpretation of gene expression data.
R source code for the method is available upon request.
在过去十年中，高通量数据集的解释仍然是计算生物学的核心挑战之一。此外，随着生物知识量的增加，越来越难以以有意义的方式整合这一大体量的知识。在本文中，我们针对这两个挑战提出了一个特殊的解决方案。
我们通过构建特定类型的分子相互作用网络（因果相互作用网络）来整合现有生物学知识。由此产生的因果图可以查询，以提出分子假说，解释高通量基因表达实验中观察到的变化。我们表明，一个简单的评分函数可以区分大量关于基因表达谱中观察到的变化的上游原因的竞争性分子假说。然后，我们开发了一种用于计算每个分数的统计显著性的分析方法。这种分析方法还有助于评估随机或敌对噪声对我们模型的预测能力的影响。
我们的结果表明，我们从已知生物学文献中构建的因果图对随机噪声以及缺失或虚假信息具有极强的稳健性。我们通过两个具体示例展示了我们的因果推理模型的强大功能，一个来自癌症数据集，另一个来自心脏肥大实验。我们得出的结论是，因果推理模型为生物学家解释基因表达数据提供了有价值的工具。
方法的 R 源代码可根据要求提供。
