构建“超级基因集”调控网络

J. Chen, Z. Yue, Michael T. Neylon, Thanh Nguyen, Nafisa Bulsara, Itika Arora, Timothy Ratliff
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引用次数: 1

摘要

在本文中,我们描述了一个新的计算框架来构建“超级基因集”-途径,注释列表和基因签名(pag),调控(r型)PAG-PAG关系。为了构建PAG,我们在一个共有的多基因PAG (mPAGs)的上游/下游聚合了单例PAG (sPAGs)。然后,我们迭代去除一个成员基因来计算其凝聚力系数(CoCo),这有助于评估超越随机机会的生物相关性程度,直到CoCo得分在特定水平上达到最大值。因此,聚合mPAG (m'PAG)和共享mPAG之间的新关系将具有不同的m'PAG-mPAG关系。我们的研究结果表明:首先,新的m'PAG具有足够高的CoCo分数,表明具有较高的生物学相关性,并且具有不同于其受调控的PAG靶标的独特基因本体注释;然而,在每一对已识别的m'PAG-mPAG关系之间存在显著丰富的共享GO注释。其次,基于噪声特性模拟,新的m' pag对数据噪声具有相对的鲁棒性。第三,通过将我们的框架应用于真实的癌症微阵列分析数据,我们证明了我们的新框架在帮助构建易于被生物学家解释的多尺度生物分子系统模型方面是有效的。
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Towards constructing “Super Gene Sets” regulatory networks
In this article, we described a new computational framework to construct “Super Gene Sets”-Pathways, Annotated list, and Gene signatures (PAGs), regulatory (r-type) PAG-PAG relationships. To construct PAGs, we aggregate singleton PAGs (sPAGs) upstream/downstream of a common shared multi-gene PAG (mPAGs). Then, we iteratively remove a member gene to calculate its Cohesion Coefficient (CoCo), which helps assess the degree of biological relevance beyond random chance, until the CoCo score achieves the maximal value at a specific level. The new relationship between aggregated mPAG (m'PAG) and the shared mPAG will, therefore, have distinct m'PAG-mPAG relationships. Our results suggest the following. First, the new m'PAGs have sufficiently high CoCo scores, suggesting high biological relevance, and distinct gene ontology annotations different from their regulated PAG targets; however, there are significant enrichments of shared GO annotations between each pair of identified m'PAG-mPAG relationships. Second, new m'PAGs are relatively robust against data noise based on noise characteristic simulations. Third, by applying our framework to real cancer microarray analysis data, we demonstrated that our new framework is effective in helping build multi-scale biomolecular systems models that are easy to interpret by biologists.
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