使用早期版本系统生物学工具箱中有一些函数还是比较有用的，汇集于此备忘，如需要可以安装早期版本查询使用。
Network Analysis and Visualization
Apply basic graph theory algorithms to Protein-Protein Interactions (PPI) and other gene networks; view network relationships using interactive maps, hierarchy plots, and pathways
Use various graph algorithms to analyze gene networks and protein-protein interactions. Represent different types of graphs using sparse matrices. Visualize graphs and network relationships using the biograph object which lets you calculate the position of nodes, draw the graph, and find relations between nodes.
Classes
biograph |
(Removed) Create biograph object |
biograph object |
(Removed) Data structure containing generic interconnected data used to implement directed graph |
Graph Theory Functions
Graph theory functions in the Bioinformatics Toolbox™ apply basic graph theory algorithms to sparse matrices. A sparse matrix represents a graph, any nonzero entries in the matrix represent the edges of the graph, and the values of these entries represent the associated weight (cost, distance, length, or capacity) of the edge. Graph algorithms that use the weight information will cancel the edge if a NaN or an Inf is found. Graph algorithms that do not use the weight information will consider the edge if a NaN or an Inf is found, because these algorithms look only at the connectivity described by the sparse matrix and not at the values stored in the sparse matrix.
Sparse matrices can represent four types of graphs:
Directed Graph — Sparse matrix, either double real or logical. Row (column) index indicates the source (target) of the edge. Self-loops (values in the diagonal) are allowed, although most of the algorithms ignore these values.
Undirected Graph — Lower triangle of a sparse matrix, either double real or logical. An algorithm expecting an undirected graph ignores values stored in the upper triangle of the sparse matrix and values in the diagonal.
Direct Acyclic Graph (DAG) — Sparse matrix, double real or logical, with zero values in the diagonal. While a zero-valued diagonal is a requirement of a DAG, it does not guarantee a DAG. An algorithm expecting a DAG will not test for cycles because this will add unwanted complexity.
Spanning Tree — Undirected graph with no cycles and with one connected component.
There are no attributes attached to the graphs; sparse matrices representing all four types of graphs can be passed to any graph algorithm. All functions will return an error on nonsquare sparse matrices.
Graph algorithms do not pretest for graph properties because such tests can introduce a time penalty. For example, there is an efficient shortest path algorithm for DAG, however testing if a graph is acyclic is expensive compared to the algorithm. Therefore, it is important to select a graph theory function and properties appropriate for the type of the graph represented by your input matrix. If the algorithm receives a graph type that differs from what it expects, it will either:
Return an error when it reaches an inconsistency. For example, if you pass a cyclic graph to the graphshortestpath function and specify Acyclic as the method property.
Produce an invalid result. For example, if you pass a directed graph to a function with an algorithm that expects an undirected graph, it will ignore values in the upper triangle of the sparse matrix.
