gsri(GSRI)
gsri()所属R语言包：GSRI
Methods for the Gene Set Regulation Index (GSRI)
基因组调控指标（GSRI方法）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate the number of differentially expressed genes in gene sets.
估计的差异表达基因的基因组。
用法----------Usage----------
gsri(exprs, groups, geneSet, names=NULL, weight=NULL, nBoot=100,
test=rowt, testArgs=NULL, alpha=0.05, grenander=TRUE, verbose=FALSE,
...)
参数----------Arguments----------
参数：exprs
Matrix or object of class ExpressionSet containing the expression intensities of the microarray. If a matrix the rows represent the genes and the columns the samples, respectively.
矩阵或对象类ExpressionSet含有芯片的表达强度。如果一个矩阵的行代表基因和列的样本，分别。
参数：groups
Factor with the assignments of the microarray samples to the groups, along which the differential effect should be estimated. Must have as many elements as exprs has samples.
与芯片组，沿差的效果，应估计样品的分配系数。必须具备作为exprs有样品的许多元素。
参数：geneSet
Optional object of class GeneSet or GeneSetCollection defining the gene set(s) used for the analysis. If missing all genes of exprs are considered to be part of the gene set. If an object of class GeneSet only these genes are considered to be part of the gene set. If an object of class GeneSetCollecton the analysis is performed for each gene set of the collection individually.
可选的对象类GeneSet或GeneSetCollection定义用于分析基因组（S）。如果失踪exprs被认为是基因组的一部分的所有基因。如果一个对象的类GeneSet只有这些基因被认为是基因组的一部分。如果一个类的对象GeneSetCollecton集合中的每个基因组进行单独的分析。
参数：names
Optional character vector with the names of the gene set(s). If missing the names are taken from the geneSet argument. Has to have as many unique elements as gene sets in the analysis.
可选的特征向量的基因组（S）的名称。如果丢失的名字取自geneSet参数。必须具有许多独特的元素，基因组分析。
参数：weight
Optional numerical vector of weights specifying the certainty a gene is part of the gene set. If NULL all genes are assumed to have the same weight. Please note that the weights are defined in a relative way and thus any kind of positive weights is feasable. Must have as many elements as eighter the genes defined in geneSet or in exprs.
可选的数值指定确定性的基因重向量，是基因组的一部分。如果NULL所有的基因被认为具有相同的重量。请注意的重量在一个相对的定义，因此，任何正权一种是feasable的的。必须有作为eighter的基因定义在geneSet或exprs的许多元素。
参数：nBoot
Integer with the number of bootstrap samples to be drawn in the calculation of the GSRI (default: 100).
引导样品的数量，在制定计算的GSRI的（默认值：100）的整数。
参数：test
A function defining the statistical test used to assess the differential effect between the groups which are given by the groups argument. In this package, a t-test (rowt) and an F-test (rowF) are already supplied, with rowt being the default. Additionally, a custom test function can be used in order to be able to include any feasible statistical test in the analysis. For details, please see the "details" section.
函数定义来评估groups参数组之间的差，效果的统计检验。在这个包中的T-测试（rowt）的一架F-测试（rowF）已经提供，与rowt是默认。此外，可用于自定义的测试功能，以便能够包括任何可行的统计分析测试。有关详情，请参阅“细节”部分。
参数：testArgs
List of optional arguments used by the test function. For details, please see the "details" section and the help for test-functions.
名单test函数使用的可选参数。有关详情，请参阅“详情”一节和帮助test-functions。
参数：alpha
Single numeric specifying the confidence level for the GSRI. The estimated GSRI is the lower bound of the (1-\sQuote{alpha})*100% confidence interval obtained from bootstrapping.
单一数字指定为对GSRI的信心水平。的的估计GSRI是下界（1 - \sQuote{alpha}）* 100％的信心区间从引导获得。
参数：grenander
Logical about whether the modified Grenander estimator for the cumulative density should be used instead of a centered ECDF. By default the modified Grenander estimator is used. For more information, please see the "details" section.
关于是否应使用，而不是一个中心厄立特里亚社区发展基金累计密度Grenander估计逻辑。默认情况下，改性Grenander估计。有关详细信息，请参阅“细节”部分。
参数：verbose
Logical indicating whether the progress of the computation should be printed to the screen (default: FALSE). Most useful if geneSet is an object of class GeneSetCollection.
逻辑表示计算的进展是否应该被打印到屏幕（默认：false）。最有用：geneSet类GeneSetCollection的对象。
参数：...
Additional arguments, including:
额外的参数，包括：
minSize:Integer specifying the minimal number of genes in a gene set in order to perform an analysis. If the gene set has less than minSize in exprs, the gene set is ignored in the analysis.
MINSIZE：整数，指定在基因组的基因数量最少，以执行分析。如果基因组少于minSizeexprs，在分析中忽略的基因组。“
nCores:Integer setting the number of cores used for the computation in combination with the multicore package for a GeneSetCollection. For details, please see the "details" section.
nCores：整数设置用于multicore包GeneSetCollection结合计算的核心。有关详情，请参阅“细节”部分。
Details
详情----------Details----------
The gsri method estimates the degree of differential expression in gene sets. By assessing the part of the distribution of p-values consistent with the null hypothesis the number of differentially expressed genes is calculated.
gsri方法估计程度差异表达的基因组。通过评估的部分P-值与零假设是一致的分布差异表达基因的数量计算。
Through non-parametric fitting of the uniform component of the p-value distribution, the fraction of regulated genes \sQuote{r} in a gene set is estimated. The GSRI \sQuote{eta} is then defined as the \sQuote{alpha*100}%-quantile of the distribution of \sQuote{r}, obtained from bootstrapping the samples within the groups. The index indicates that with a probability of (1-\sQuote{alpha})% more than a fraction of \sQuote{eta} genes in the gene set is differentially expressed. It can also be employed to test the hypothesis whether at least one gene in a gene set is regulated. Further, different gene sets can be compared or ranked according to the estimated amount of regulation.
通过非参数拟合p值分布均匀的组成部分，调节基因的一小部分\sQuote{r}在基因组估计。在GSRI\sQuote{eta}然后定义作为\sQuote{alpha*100}％位数的分配\sQuote{r}引导组内的样本获得的。该指数表明，概率(1-\sQuote{alpha})％\sQuote{eta}基因在基因组的一小部分差异表达。它也可以被用来测试是否至少有一个在基因组的基因调控假说。此外，不同的基因组进行比较或排名根据监管的估计数额。
Assessing the differential effect is based on p-values obtained from statistical testing at the level of individual genes between the groups. The GSRI approach is independent of the underlying test and can be chosen according to the experimental design. With the t-test (rowt) and F-test (rowF) two widely used statistical test are already part of the package. Additional tests can easily used which are passed with the test argument to the gsri method. For details on how to implement custom test functions, please refer to the help of rowt and rowF or the vignette of this package.
差影响评估的基础上获得的个人基因组之间的水平从统计检验的p值。 GSRI方法是独立的基础测试，并根据实验设计可以选择。通过t检验（rowt）F-检验（rowF）两种广泛使用的统计测试已经包的一部分。额外的测试，可以很容易地使用test参数传递给gsri方法。对于如何实现自定义的测试功能的详细信息，请参阅帮助rowt和rowF或这个包的小插曲。
The GSRI approach further allows weighting the influence of individual genes in the estimation. This can be beneficial including for example the certainty that genes are part of a certain gene set derived from experimental findings or annotations.
进一步GSRI方法允许加权估算单个基因的影响。这可以是有益的，例如，包括基因肯定是某基因从实验结果或注释的一部分。
Defining gene sets is available through the GSEABase package which provides the GeneSet and GeneSetCollection classes a single or multiple gene sets, respectively. This ensures a powerful approach for obtaining gene sets from data objects, data bases, and other bioconductor packages. For details on how to define or retrieve gene sets, please refer to the documentation of the GSEABase package, with a special focus on the GeneSet and GeneSetCollection classes.
定义基因组是可以通过GSEABase包GeneSet和GeneSetCollection类有一个或多个基因集，分别为。这确保了强大的方法获得基因集的数据对象，数据碱基，和其他bioconductor的包。对于如何界定或检索基因集的详细信息，请参阅GSEABase包的文档，特别注重对GeneSet和GeneSetCollection类。
The distribution of the p-values of a gene set is assessed in the cumulative density. In addition to a symmetrical empirical cumulative density function (ECDF), the modified Grenander estimator based on the assumption about the concave shape of the cumulative density is implemented and used by default. While the modified Grenander estimator reduces the variance and makes the approach more stable especially for small gene set, it underestimates the number of regulated genes and thus leads to conservative estimates.
评估累计密度分布的一个基因组的p值。除了对称的经验累积分布函数（厄立特里亚社区发展基金），累计密度凹造型的假设的基础上，的修改Grenander估计是默认情况下，实施和使用。虽然的修改Grenander估计减少的变异，使方法更稳定，特别是对于小基因组，它低估的调节基因的数量，从而导致保守的估计。
In the case that the computation is performed for several gene sets in the form of a GeneSetCollection object, it can be parallelized with the multicore package. Please note that this package is not available on all platforms. Using its capabilities requires attaching multicore prior to the calculation and specification of the nCores argument. For further details, please refer to the documentation of the multicore package. This may be especially relevant in the case that specific seed values for the bootstrapping are of interest.
在几个基因组在GeneSetCollection对象的形式进行计算的情况下，它可以并行multicore包。请注意，这个包是不是在所有平台上。使用它的功能需要附加multicore前nCores参数的计算和规范。欲知详情，请参阅multicore包的文档。这可能是相关的，尤其是在利益的情况下，特定的种子值的自举。
值----------Value----------
An object of class Gsri with the slots:
一个对象的类Gsri插槽：
result: Data frame containing the results of the GSRI
result：数据框包含的GSRI结果
cdf: List of data frames containing the ECDF of the
cdf：数据框包含的厄立特里亚社区发展基金名单
parms: List containing the parameter values used in
parms：名单中使用的参数值
For details, please see the help for the Gsri class.
有关详情，请参阅帮助Gsri类。
方法----------Methods----------
Analysis for all genes of exprs part of the gene set:
exprs部分基因组的所有基因的分析：
Analysis for one gene set, defined as an object of class GeneSet:
一个基因组的分析，定义为一个对象类GeneSet：
Analysis for several gene sets, defined as an object of class GeneSetCollection:
几个基因组的分析，定义为一个对象类GeneSetCollection：
In this case parallel computing capabilities provided by the multicore package may be available, depending on the platform.
在这种情况下multicore包的并行计算能力提供可能，取决于平台。
注意----------Note----------
The standard deviation of the estimated number of regulated genes as well as the GSRI are obtained through bootstrapping. Thus, the results for these two parameters may differ slightly for several realizations, especially for small numbers of bootstraps (nBoot). Setting the seed of the random number generator avoids this problem and yields exactly the same results for several realizations.
标准偏差的调节基因的估计数目，以及在GSRI获得通过引导。因此，这两个参数的结果可能会略有不同，几个实现，特别是白手起家的小号码（nBoot）。设置随机数发生器的种子，避免了这个问题，究竟会产生几个实现了相同的结果。
作者（S）----------Author(s)----------
Julian Gehring
Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>
参见----------See Also----------
Package: GSRI-package
包装：GSRI-package
Class: Gsri
类别：Gsri
Methods: gsri getGsri getCdf getParms export sortGsri plot show summary readCls readGct
方法：gsrigetGsrigetCdfgetParmsexportsortGsriplotshowsummary<X >readCls
举例----------Examples----------
## Simulate expression data for a gene set of[＃模拟为一个基因组表达数据]
## 100 genes, 20 samples (10 treatment, 10 control)[＃100个基因，20个样本（10治疗，10控制）]
## and 30 regulated genes[＃和30个调节基因]
set.seed(1)
exprs <- matrix(rnorm(100*20), 100)
exprs[1:30,1:10] <- rnorm(30*10, mean=2)
rownames(exprs) <- paste("g", 1:nrow(exprs), sep="")
groups <- factor(rep(1:2, each=10))
## Estimate the number of differentially expressed genes[＃估计差异表达基因的数目。]
res <- gsri(exprs, groups)
res
## Perform the analysis for different gene set[＃执行不同的基因组分析]
library(GSEABase)
gs1 <- GeneSet(paste("g", 25:40, sep=""), setName="set1")
gs2 <- GeneSet(paste("g", seq(1, nrow(exprs), by=5), sep=""), setName="set2")
gsc <- GeneSetCollection(gs1, gs2)
res2 <- gsri(exprs, groups, gs1)
res3 <- gsri(exprs, groups, gsc, verbose=TRUE)
summary(res2)
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注：
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发表于 2012-2-25 21:19:07
