Hanoudi Samer, Donato Michele, Draghici Sorin
Department of Computer Science, Wayne State University, Detroit, MI, United States of America.
Department of Obstetrics and Gynecology, Detroit, MI, United States of America.
PLoS One. 2017 May 9;12(5):e0176950. doi: 10.1371/journal.pone.0176950. eCollection 2017.
A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights.
生命科学研究中的一个主要挑战是理解给定表型所涉及的机制。为了理解诸如疾病机制、免疫系统对各种挑战的反应以及药物作用机制等基本且非常重要的现象，需要具备识别正确机制的能力。当前的数据分析方法侧重于使用基因的倍数变化和/或p值来识别差异表达（DE）基因。这种方法的主要缺点在于：i）它没有考虑基因之间的相互作用；ii）其结果对所使用阈值的选择敏感；iii）这种方法产生的基因集并不总是有助于形成机制假设。在此，我们提出一种能够构建可被视为假定机制的基因网络的方法。通过这种方法构建的假定机制不仅限于DE基因集，还考虑了所有已知且相关的基因-基因相互作用。我们分析了三个真实数据集，其表型原因以及真实机制均已知。我们表明，该方法应用于小鼠微阵列数据集时能够识别出正确的机制。我们将我们方法的结果与经典方法的结果进行了比较，结果表明我们的方法能产生更有意义的生物学见解。
