probeSetSummary(GOstats)
probeSetSummary()所属R语言包：GOstats
Summarize Probe Sets Associated with a hyperGTest Result
总结与hyperGTest结果相关的探针组
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given the result of a hyperGTest run (an instance of GOHyperGResult), this function lists all Probe Set IDs associated with the selected Entrez IDs annotated at each significant GO term in the test result.
给定的结果hyperGTest运行（一个实例GOHyperGResult），此功能列出所有探针设置标识选定Entrez的标识标注在每个测试结果显着好术语关联。
用法----------Usage----------
probeSetSummary(result, pvalue, categorySize, sigProbesets)
参数----------Arguments----------
参数：result
A GOHyperGResult instance. This is the output of the hyperGTest function when testing the GO category.
一个GOHyperGResult实例。这是hyperGTest函数的输出，测试时的GO类别。
参数：pvalue
Optional p-value cutoff. Only results for GO terms with a p-value less than the specified value will be returned. If omitted, pvalueCutoff(result) is used.
可选的p值截止。结果仅比指定值p值的条款将被退回。如果省略，pvalueCutoff(result)使用。
参数：categorySize
Optional minimum size (number of annotations) for the GO terms. Only results for GO terms with categorySize or more annotations will be returned. If omitted, no category size criteria will be used.
可选的最小尺寸（注释）的GO术语。为好注释categorySize或更多的计算结果仅将被退回。如果省略，没有类别大小的标准将被使用。
参数：sigProbesets
Optional vector of probeset IDs. See details for more information.
可选probeset标识的向量。详情请参阅更多信息。
Details
详情----------Details----------
Usually the goal of doing a Fisher's exact test on a set of significant probesets is to find pathways or cellular activities that are being perturbed in an experiment. After doing the test, one usually gets a list of significant GO terms, and the next logical step might be to determine which probesets contributed to the significance of a certain term.
一般做上显著probesets集的Fisher的精确检验的目标是找到途径或单元的活动，正在实验中的扰动。做测试后，往往会获得显着的GO术语列表，下一个逻辑步骤可能是确定这probesets贡献一定期限的意义。
Because the input for the Fisher's exact test consists of a vector of unique Entrez Gene IDs, and there may be multiple probesets that interrogate a particular transcript, the ouput for this function lists all of the probesets that map to each Entrez Gene ID, along with an indicator that shows which of the probesets were used as input.
因为，费舍尔的精确检验的输入包括一个独特的Entrez的基因标识向量，并可能存在多个probesets，询问1特别谈话，在此功能的输出中列出的所有probesets的，图每个Entrez的基因身份证，连同被用来作为一个指标，显示的probesets输入。
The rationale for this is that one might not be able to assume a given probeset actually interrogates the intended transcript, so it might be useful to be able to check to see what other similar probesets are doing.
这种情况的原因是，未必能够承担实际上是一个给定的probeset询问预期的成绩单，所以它可能是有用的，能够检查，看看做什么其他类似probesets的。
Because one of the first steps before running hyperGTest is to subset the input vectors of geneIds and universeGeneIds, any information about probeset IDs that interrogate the same gene transcript is lost. In order to recover this information, one can pass a vector of probeset IDs that were considered significant. This vector will then be used to indicate which of the probesets that map to a given GO term were significant in the original analysis.
因为前运行hyperGTest的第一个步骤是子集的geneIds和universeGeneIds输入向量，任何有关询问相同的基因转录的probeset的ID信息就会丢失。为了恢复这一信息，可以通过一个被认为是显着的probeset标识的向量。然后将这个向量用来表示映射到一个给定的GO术语在原来的分析具有重要意义的probesets。
值----------Value----------
A list of data.frame. Each element of the list corresponds to one of the GO terms (the term is provides as the name of the element). Each data.frame has three columns: the Entrez Gene ID (EntrezID), the probe set ID (ProbeSetID), and a 0/1 indicator of whether the probe set ID was provided as part of the initial input (selected)
一个listdata.frame。列表中的每个元素对应的GO术语（术语是作为元素的名称提供）之一。每个data.frameEntrez基因ID（EntrezID），探针组ID（ProbeSetID），以及是否提供了一部分探针组ID 0/1指标有三列：初始输入（selected）
Note that this 0/1 indicator will only be correct if the 'geneId' vector used to construct the GOHyperGParams object was a named vector (where the names are probeset IDs), or if a vector of 'sigProbesets' was passed to this function.
请注意，这个0/1指示灯只会是正确的，如果“geneId”向量用于构建GOHyperGParams对象是一个名为向量（名称probeset标识），或者通过一个“sigProbesets”向量此功能。
作者（S）----------Author(s)----------
S. Falcon and J. MacDonald
举例----------Examples----------
## Fake up some data[＃假了一些数据]
library("hgu95av2.db")
library("annotate")
prbs <- ls(hgu95av2GO)[1:300]
## Only those with GO ids[＃只有那些好IDS]
hasGO <- sapply(mget(prbs, hgu95av2GO), function(ids)
if(!is.na(ids) && length(ids) > 1) TRUE else FALSE)
prbs <- prbs[hasGO]
prbs <- getLL(prbs, "hgu95av2")
## remove duplicates, but keep named vector[＃删除重复，但保持命名为向量]
prbs <- prbs[!duplicated(prbs)]
## do the same for universe[＃做相同的宇宙]
univ <- ls(hgu95av2GO)[1:5000]
hasUnivGO <- sapply(mget(univ, hgu95av2GO), function(ids)
if(!is.na(ids) && length(ids) > 1) TRUE else FALSE)
univ <- univ[hasUnivGO]
univ <- unique(getLL(univ, "hgu95av2"))
p <- new("GOHyperGParams", geneIds=prbs, universeGeneIds=univ,
ontology="BP", annotation="hgu95av2", conditional=TRUE)
## this part takes time...[＃这部分需要时间......]
if(interactive()){
hyp <- hyperGTest(p)
ps <- probeSetSummary(hyp, 0.05, 10)
}
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。
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发表于 2012-2-25 20:55:47
