gseaSignatures(phenoTest)
gseaSignatures()所属R语言包：phenoTest
Compute ES (enrichment scores) and es.sim (simulated enrichment scores) for given phenotipic variable(s) and signature(s).
计算ES（富集成绩）和es.sim给予phenotipic变量（S）和签名（S）（模拟富集分）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes the first step in the process of obtaining a GSEA-like plot. It computes the enrichment scores and simulated enrichment scores for each variable and signature. The output will usually be used as input for the gseaSignificance function. An important parameter of the function is logScale. Its default value is TRUE which means that by default the provided scores (i.e. fold changes, hazard ratios) will be log scaled. Remember to change this parameter to FALSE if your scores are already log scaled. The getEs, getEsSim, getFc, getHr and getFcHr methods can be used to acces each subobject. For more information please visit the man pages of each method.
此函数计算获得GSEA般的图过程中的第一步。它计算富集分数和模拟富集分数为每个变量和签名。输出通常会被用来为gseaSignificance函数的输入。一个重要的参数是函数的logScale。这意味着，默认情况下提供的分数（即倍的变化，风险比率）将登录规模，它的默认值是TRUE。记得要改变这个参数为false，如果你的分数已经登录缩放。 getEs，getEsSim，getFc，getHr和getFcHr方法可以用来ACCES每个子对象。欲了解更多信息，请访问每个方法的手册页。
用法----------Usage----------
gseaSignatures(x,signatures,logScale=TRUE,absVals=FALSE,averageRepeats=FALSE,B=1000,mc.cores=1,test='perm')
参数----------Arguments----------
参数：x
ePhenoTest, numeric or matrix object containing hazard ratios or fold changes.
ePhenoTest，numeric或matrix对象，其中包含的危险比或倍数变化。
参数：signatures
character or list object containing the names of the genes that belong to each signature.
字符或列表对象包含的基因属于每个签名的名称。
参数：logScale
if values should be log scaled.
如果值应该是登录缩放。
参数：absVals
if TRUE fold changes and hazard ratios that are negative will be turned into positive before starting the process. This is useful when genes can go in both directions.
如果真正的fold change和风险比率为负将变成阳性，才开始这一进程。这是非常有用的基因时，可以在两个方向。
参数：averageRepeats
if x is of class numeric and has repeated names (several measures for some indivdual names) we can average the measures of the same names.
如果x是数字类和重复名称（某些蹦床网上单人的名字的几项措施），我们可以平均名称相同的措施。
参数：B
number of simulations to perform.
数字模拟执行。
参数：mc.cores
number of processors to use.
处理器使用。
参数：test
the test that will be used. 'perm' stands for the permutation based method, 'wilcox' stands for the wilcoxon test (this is the fastest one) and 'ttperm' stands for permutation t test.
将用于测试。 “烫”为代表的置换的方法，“威尔科克斯表示Wilcoxon检验（这是最快的国家之一）和”ttperm置换t检验。
Details
详情----------Details----------
The following preprocessing was done on the provided scores (i.e. fold changes, hazard ratios) to avoid errors during the enrichment score computation: -When having two scores with the same name its average was used. -Zeros were removed. -Scores without names (which can not be in any signature) removed. -Non complete cases (i.e. NAs, NaNs) were removed. ES score was calculated for each signature and variable (see references). If parameter test is 'perm' the signature was permutted and the ES score was recalculated (this happened B times for each variable, 1000 by default). If test is 'wilcox' a wilcoxon test in which we test the fact that the average value of the genes that do belong to our signtaure is different from the average value of the genes that do not belong to our signature will be performed. If test is 'ttperm' a permutation t-test will be used. Take into account that the final plot will be different when 'wilcox' is used.
所提供的分数（即倍的变化，危险比）以下预处理做是为了避免在浓缩得分计算错误：当具有相同名称的两个分数的平均使用。 - 零点被拆除。没有名字的分数（不能在任何签字）删除。 - 非完整的情况下（即NAS，NaN的）被拆除。 ES的得分计算出每个签名和变量（请参阅参考资料）。如果参数test是烫发签名permutted和ES的得分计算（此为每个变量发生的B倍，默认情况下，1000）。如果test是“威尔科克斯Wilcoxon检验中，我们测试一个事实，即平均价值的基因，属于我们的signtaure是从不同的基因不属于我们的签名平均值将执行。 test如果是“ttperm”将用于置换t检验。考虑到使用时，“威尔科克斯”最后的图会有所不同。
作者（S）----------Author(s)----------
Evarist Planet
参考文献----------References----------
www.pnas.org/cgi/doi/10.1073/pnas.0506580102
举例----------Examples----------
#load epheno object[加载epheno对象]
data(epheno)
epheno
#we construct two signatures[我们建立两个签名]
sign1 <- sample(featureNames(epheno))[1:20]
sign2 <- sample(featureNames(epheno))[50:75]
mySignature <- list(sign1,sign2)
names(mySignature) <- c('My first signature','My preferred signature')
#run gsea functions[运行GSEA功能]
#my.gseaSignatures <- gseaSignatures(x=epheno,signatures=mySignature,B=100,mc.cores=1)[my.gseaSignatures < - gseaSignatures（X = epheno，签名mySignature，乙= 100，mc.cores = 1）]
#my.gseaSignificance <- gseaSignificance(my.gseaSignatures)[my.gseaSignificance < - gseaSignificance（my.gseaSignatures）]
#my.summary <- summary(my.gseaSignificance)[my.summary < - 摘要（my.gseaSignificance）]
#my.summary [my.summary]
#plot(my.gseaSignatures,my.gseaSignificance)[图（my.gseaSignatures，my.gseaSignificance）]
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 11:04:54
