GSRI-package(GSRI)
GSRI-package()所属R语言包：GSRI
Gene Set Regulation Index (GSRI) package
基因组规例“指数（GSRI）的包
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI).
GSRI包估计基因组的差异表达基因的数量，利用基因组规例“指数（GSRI）的概念。
Details
详情----------Details----------
The GSRI approach estimates the number of differentially expressed genes in gene sets. It is independent of the underlying statistical test used for assessing the differential effect of genes and does not require any cut-off values for the distinction between regulated and unregulated genes. The approach is based on the fact that p-values obtained from a statistical test are uniformly distributed under the null hypothesis and are shifted towards zero in case of the alternative hypothesis.
GSRI方法估计基因组的差异表达基因的数量。它是独立于底层的统计测试用于评估基因的差的效果，并且不需要任何调节和无管制的基因之间的区别截止值。该方法是基于对从统计检验得到的P-值是均匀分布的假设下，在另一种假设的情况下向零转移的事实。
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。
For details of the method, an application to experimental data, and a comparison with related approaches, see Bartholome et al., 2009.
的方法，实验数据的应用，并与相关方法比较的详细信息，请参阅Bartholome等。，2009。
The package is published under the GPL-3 license.
包发布在GPL下-3许可证。
作者（S）----------Author(s)----------
Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer
Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>
参考文献----------References----------
Estimation of gene induction enables a relevance-based ranking of gene sets, Journal of Computational Biology: A Journal of Computational Molecular Cell Biology 16, no. 7 (July 2009): 959-967. http://www.liebertonline.com/doi/abs/10.1089/cmb.2008.0226
packages:
les: Identifying Loci of Enhanced Significance in Tiling Microarray Data http://bioconductor.org/help/bioc-views/release/bioc/html/les.html
fdrtool: Estimation and Control of (Local) False Discovery Rates. http://CRAN.R-project.org/package=fdrtool
genefilter: methods for filtering genes from microarray experiments. http://bioconductor.org/help/bioc-views/release/bioc/html/genefilter.html
参见----------See Also----------
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 21:19:00
