Visualizing plant metabolomic correlation networks using clique-metabolite matrices
Visualizing plant metabolomic correlation networks using clique-metabolite matrices
复制标题
DOI：
10.1093/bioinformatics/17.12.1198
复制
发表时间：
2001-12-01
期刊：
影响因子：
5.8
通讯作者：
Fiehn, O
中科院分区：
文献类型：
--
作者：
Kose, F;Weckwerth, W;Fiehn, O
动机：今天，生物样品中的代谢物水平可以使用多平行，快速和精确的代谢组学方法来确定。可以搜索各种代谢物水平之间的相关性，以获得有关代谢联系的信息。这种相关性是直接酶促转化和转录或生物化学过程的间接细胞调节的净结果。为了以图形方式可视化从相关性列表导出的代谢网络，每个代谢物对可以被表示为由边连接的顶点。然而，图的复杂性随着边和顶点的数量而迅速增加。为了从代谢物相关网络中获得结构信息，需要提高清晰度。结果：为了实现这种清晰度，三种算法相结合。首先，生成线性代谢物相关性的列表，其可以被视为一组边缘对（或2-团）。其次，提出了一种分支定界算法，通过合并次极大团来寻找所有的极大团。由于团分配过程，避免了不必要的次极大团的生成，以保持高效率。指出了与Bron-Kerbosch算法的异同点。最后，代谢物相关性网络通过分类以最小化连接不同团和代谢物的线的长度的团-代谢物矩阵来可视化。生化假设的例子给出，可以建立从解释这样的集团矩阵。
Motivation: Today, metabolite levels in biological samples can be determined using multiparallel, fast, and precise metabolomic approaches. Correlations between the levels of various metabolites can be searched to gain information about metabolic links. Such correlations are the net result of direct enzymatic conversions and of indirect cellular regulation over transcriptional or biochemical processes. In order to visualize metabolic networks derived from correlation lists graphically, each metabolite pair may be represented as vertices connected by an edge. However, graph complexity rapidly increases with the number of edges and vertices. To gain structural information from metabolite correlation networks, improvements in clarity are needed.Results: To achieve this clarity, three algorithms are combined. First, a list of linear metabolite correlations is generated that can be regarded as a set of pairs of edges (or as 2-cliques). Next, a branch-and-bound algorithm was developed to find all maximal cliques by combining submaximal cliques. Due to a clique assignment procedure, the generation of unnecessary submaximal cliques is avoided in order to maintain high efficiency. Differences and similarities to the Bron-Kerbosch algorithm are pointed out. Lastly, metabolite correlation networks are visualized by clique-metabolite matrices that are sorted to minimize the length of lines that connect different cliques and metabolites. Examples of biochemical hypotheses are given that can be built from interpretation of such clique matrices.