shortestPath(GOstats)
shortestPath()所属R语言包：GOstats
Shortest Path Analysis
最短路径分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The shortest path analysis was proposed by Zhou et. al. The basic computation is to find the shortest path in a supplied graph between two Entrez Gene IDs. Zhou et al claim that other genes annotated along that path are likely to have the same GO annotation as the two end points.
最短路径分析，提出了由周等。等。基本的计算是在两Entrez基因标识提供图找到的最短路径。 Zhou等人声称，沿着这条道路的注解其他基因可能有相同的两个终点GO注释。
用法----------Usage----------
shortestPath(g, GOnode, mapfun=NULL, chip=NULL)
参数----------Arguments----------
参数：g
An instance of the graph class.
graph类的一个实例。
参数：GOnode
A length one character vector specifying the GO node of interest.
指定长度字符向量权益的节点。
参数：mapfun
A function taking a character vector of GO IDs as its only argument and returning a list of character vectors of Enterz Gene IDs annotated at each corresponding GO ID. The function should behave similarly to mget(x, go2egmap, ifnotfound=NA), that is, NA should be returned if a specified GO ID has no Entrez ID mappings. See details for the interaction of mapfun and chip.
函数的好标识的特征向量作为其唯一的参数和返回Enterz基因标识标注在每个相应的好ID的特征向量。该函数的行为同样mget(x, go2egmap, ifnotfound=NA)，就是NA应返回指定好ID有没有Entrez的ID映射。详情请参阅为mapfun和chip的互动。
参数：chip
The name of a DB-based annotation data package (the name will end in ".db"). This package will be used to generate an Entrez ID to GO ID mapping instead of mapfun.
一个DB-基于注解的数据包（名称将结束“。DB”）的名称。这个包将被用于产生Entrez的ID，ID映射，而不是去mapfun。
Details
详情----------Details----------
The algorithm implemented here is quite simple. All Entrez Gene identifiers that are annotated at the GO node of interest are obtained. Those that are found as nodes in the graph are retained and used for the computation. For every pair of nodes at the GO term the shortest path between them is computed using sp.between from the RBGL package.
在这里实现的算法很简单。得到所有Entrez基因注明权益的节点标识符。那些被发现图中的节点被保留，并用于计算。对于每一个节点在GO术语对它们之间的最短路径计算使用sp.between从RBGL包。
There is a presumption that the graph is undirected. This restriction could probably be lifted if there was some reason for it - a patch would be gratefully accepted.
有一个推定该图是undirected。如果有一些原因，很可能被取消此限制 - 一个补丁将欣然接受。
The mapping of GO node to Entrez ID is achieved in one of three ways:
Entrez的编号好节点的映射实现的三种方式之一：
If mapfun is provided, it will be used to perform the needed lookups. In this case, chip will be ignored.
如果mapfun提供，将用于执行所需的查找。在这种情况下，chip将被忽略。
If chip is provided and mapfun=NULL, then the needed lookups will be done based on the GO to Entrez mappings encapsulated in the specified annotation data package. This is the recommended usage.
如果chip是mapfun=NULL，则需要查找将基于Entrez的映射在指定的注释数据包封装好做。这是推荐使用。
If mapfun and chip are NULL or missing, then the function will attempt to load the GO package (the environment-based package, distinct from GO.db). This package contains a legacy environment mapping GO IDs to Entrez IDs. If the GO package is not available, an error will be raised. Omitting both mapfun and chip is not recommended as it is not compatible with the DB-based annotation data packages.
如果mapfun和chip是NULL或丢失，那么该函数将尝试加载GO套件（基于环境的软件包，从GO.db不同）。这个软件包包含一个遗留的环境映射的ID Entrez的标识。如果没有好包，一个错误将得到提升。省略双方mapfun和chip不建议，因为它是不兼容的DB-基于标注的数据包。
值----------Value----------
The return values is a list with the following components:
返回值是由以下部分组成的列表：
参数：shortestpaths
A list of the ouput from sp.between. The names are the names of the nodes used as the two endpoints
从sp.between列表的输出中。的名称是作为两个端点的节点的名称
参数：nodesUsed
A vector of the Entrez Gene IDs that were both found at the GO term of interest and were nodes in the supplied graph, g. These were used to compute the shortest paths.
Entrez基因标识均好长远利益，并在所提供的图形，g节点向量。这些被用来计算最短路径。
参数：nodesNotUsed
A vector of Entrez Gene IDs that were annotated at the GO term, but were not found in the graph g.
Entrez基因标识，在GO术语注释，但向量图g均未发现。
作者（S）----------Author(s)----------
R. Gentleman
参考文献----------References----------
of gene expression data, by X. Zhou and M-C J. Kao and W. H. Wong,
参见----------See Also----------
sp.between
sp.between
举例----------Examples----------
library("hgu95av2.db")
library("RBGL")
set.seed(321)
uniqun <- function(x) unique(unlist(x))
goid <- "GO:0005778"
egIds <- uniqun(mget(uniqun(hgu95av2GO2PROBE[[goid]]),
hgu95av2ENTREZID))
v1 <- randomGraph(egIds, 1:10, .3, weights=FALSE)
## Since v1 is random, it might be disconnected and we need a[V1＃由于是随机的，它可能会被中断，我们需要一个]
## connected graph to guarantee the existence of a path.[＃连接图，以保证存在一个路径。]
c1 <- connComp(v1)
largestComp <- c1[[which.max(sapply(c1, length))]]
v2 <- subGraph(largestComp, v1)
a1 <- shortestPath(v2, goid, chip="hgu95av2.db")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-2-25 20:55:55
