Bioinformatics and Computational Biology, Electrical and Computer Engineering Department, Iowa State University, Ames, IA 50011, USA.
Bioinformatics. 2012 Apr 1;28(7):947-54. doi: 10.1093/bioinformatics/bts039. Epub 2012 Feb 4.
Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events.
The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies.
We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material.
Supplementary data are available at Bioinformatics online.
分析组学实验会生成根据特定行为（如对压力或其他信号的反应变化）选择的实体（基因、代谢物等）列表。这些列表的功能解释通常使用功能注释（如基因本体论术语和途径成员）的类别富集测试。这种方法没有考虑生化途径的连接结构或事件的因果方向性。
本文中描述的 Omics Response Group (ORG) 方法，使用网络中流量的统计模型，在代谢途径和调控网络的上下文中解释组学列表。所有响应组的统计结果都以新颖的途径流量图进行可视化。统计测试基于独立且相同的指数分布随机游动通过途径的 Erlang 分布模型。作为概念验证，我们将我们的方法应用于大肠杆菌转录组数据集，其中我们证实了大肠杆菌对脂多糖剥夺的转录反应的常识。主要反应与渗透胁迫有关，我们还能够检测到文献支持的新反应。我们还将我们的方法应用于拟南芥表达数据集，该数据集来自脱落酸研究。在这两种情况下，常规途径富集测试都没有检测到任何内容，而我们的方法发现了超出原始研究的生物学过程。
我们在 http://ecoserver.vrac.iastate.edu/pathwayflow 创建了一个交互式 ORG 网络工具的原型（源代码可从 https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow 获得）。原型与补充材料中的其他图和表一起进行了描述。
补充数据可在生物信息学在线获得。
