romer(limma)
romer()所属R语言包：limma
Rotation Gene Set Enrichment Analysis
自转基因组富集分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Gene set enrichment analysis for linear models using rotation tests (ROtation testing using MEan Ranks).
使用旋转试验（旋转测试使用均值队伍）的线性模型的基因组富集分析。
用法----------Usage----------
romer(iset,y,design,contrast=ncol(design),array.weights=NULL,block=NULL,correlation,set.statistic="mean",nrot=9999)
参数----------Arguments----------
参数：iset
list of indices specifying the rows of y in the gene sets. The list can be made using symbols2indices.
列表指定y在基因组的行的索引。列表中可以使用symbols2indices。
参数：y
numeric matrix giving log-expression values.
数字矩阵提供log表达式的值。
参数：design
design matrix
设计矩阵
参数：contrast
contrast for which the test is required. Can be an integer specifying a column of design, or else a contrast vector of length equal to the number of columns of design.
对比测试是必需的。可以是一个整数，指定的列design，否则相反向量的长度等于design列数。
参数：array.weights
optional numeric vector of array weights.
可选的数字阵列权重向量。
参数：block
optional vector of blocks.
块可选向量。
参数：correlation
correlation between blocks.
块之间的相关性。
参数：set.statistic
statistic used to summarize the gene ranks for each set. Possible values are "mean", "floormean" or "mean50".
统计总结每套基因行列。可能值"mean"，"floormean"或"mean50"。
参数：nrot
number of rotations used to estimate the p-values.
用来估计p值旋转。
Details
详情----------Details----------
This function implements the ROMER procedure described by Majewski et al (2010). romer tests a hypothesis similar to that of Gene Set Enrichment Analysis (GSEA) (Subramanian et al, 2005) but is designed for use with linear models. Like GSEA, it is designed for use with a database of gene sets. Like GSEA, it is a competitive test in that the different gene sets are pitted against one another. Instead of permutation, it uses rotation, a parametric resampling method suitable for linear models (Langsrud, 2005). romer can be used with any linear model with some level of replication.
此功能实现了罗默马耶夫斯基等（2010）所描述的过程。 romer测试类似的基因组富集分析（GSEA）（Subramanian等人，2005年）的一个假设，但与使用线性模型设计。 GSEA一样，它被设计为一个基因组数据库的使用。 GSEA一样，它是有竞争力的测试，在不同的基因组对一个进站。而不是置换，它使用的旋转，一个参数的重采样方法适用于线性模型（Langsrud，2005年）。 romer可以用线性模型的任何部分复制水平。
Curated gene sets suitable for use with romer can be downloaded from http://bioinf.wehi.edu.au/software/MSigDB/. These lists are based on the molecular signatures database from the Broad Institute, but with gene symbols converted to offical gene symbols, separately for mouse and human.
curated基因集适合romer可以从http://bioinf.wehi.edu.au/software/MSigDB/的下载使用。这些名单是根据分子签名的Broad研究所，但转换到官方的基因符号，分别用于小鼠和人类的基因符号数据库。
In the output, p-values are given for each set for three possible alternative hypotheses. The alternative "up" means the genes in the set tend to be up-regulated, with positive t-statistics. The alternative "down" means the genes in the set tend to be down-regulated, with negative t-statistics. The alternative "mixed" test whether the genes in the set tend to be differentially expressed, without regard for direction. In this case, the test will be significant if the set contains mostly large test statistics, even if some are positive and some are negative. The first two alternatives are appropriate if you have a prior expection that all the genes in the set will react in the same direction. The "mixed" alternative is appropriate if you know only that the genes are involved in the relevant pathways, without knowing the direction of effect for each gene.
在输出中，p值给每个组三个可能的替代假说。替代的“向上”是指在一组的基因往往是积极的t-统计，上调。替代“下降”是指在一组的基因往往是负的t-统计量的下调。替代的“混合”的测试是否往往差异表达方面没有方向，在一组的基因。在这种情况下，该测试将是重要的，如果集合中包含大多是大型的测试统计，即使有些是积极的，有些是负面的。前两个备选方案是适当的，如果你有一个事先expection集合中的所有基因，将反应在同一方向。 “混合”的另一种方法是适当的，如果你知道，只有该基因参与有关途径，不知道每个基因的作用方向。
Note that romer estimates p-values by simulation, specifically by random rotations of the orthogonalized residuals. This means that the p-values will vary slightly from run to run. To get more precise p-values, increase the number of rotations nrot. The strategy of random rotations is due to Langsrud (2005).
注意romer估计p值由模拟正交残差随机轮换专门。这意味着，P-值将略有不同运行运行。为了得到更精确的p值，增加旋转nrot。随机轮换的策略是由于Langsrud（2005年）。
The argument set.statistic controls the way that t-statistics are summarized to form a summary test statistic for each set. In all cases, genes are ranked by moderated t-statistic. If set.statistic="mean", the mean-rank of the genes in each set is the summary statistic. If set.statistic="floormean" then negative t-statistics are put to zero before ranking for the up test, and vice versa for the down test. This improves the power for detecting genes with a subset of responding genes. If set.statistics="mean50", the mean of the top 50% ranks in each set is the summary statistic. This statistic performs well in practice but is slightly slower to compute.
参数set.statistic控制，t-统计汇总，形成汇总检验统计量为每一套方式。在所有情况下，基因版主t-统计排名。如果set.statistic="mean"，每套基因的平均排名是汇总统计。如果set.statistic="floormean"然后负的t-统计，排名前为零的后续试验，反之亦然向下测试。这提高了基因检测与响应基因的一个子集的权力。如果set.statistics="mean50"，平均每套在50％的顶尖行列，是汇总统计。这一统计数字在实践中表现良好，但计算速度稍慢。
值----------Value----------
Numeric matrix giving p-values and the number of matched genes in each gene set. Rows correspond to gene sets. There are four columns giving the number of genes in the set and p-values for the alternative hypotheses mixed, up or down.
数字矩阵P-值和匹配在每个基因组的基因数量。行对应的基因套。有四列，使基因组中的p-值和替代混合假说，向上或向下的数量。
作者（S）----------Author(s)----------
Yifang Hu and Gordon Smyth
参考文献----------References----------
Rotation tests. Statistics and Computing 15, 53-60
Rotation testing in gene set enrichment analysis for small direct comparison experiments. Stat Appl Genet Mol Biol, Article 34.
Opposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells. Blood, published online 5 May 2010. http://www.ncbi.nlm.nih.gov/pubmed/20445021
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550
参见----------See Also----------
topRomer, symbols2indices, roast, wilcoxGST
topRomer，symbols2indices，roast，wilcoxGST
An overview of tests in limma is given in 08.Tests.
的概述在limma的测试是在08.Tests。
举例----------Examples----------
y <- matrix(rnorm(100*4),100,4)
design <- cbind(Intercept=1,Group=c(0,0,1,1))
iset <- 1:5
y[iset,3:4] <- y[iset,3:4]+3
iset1 <- 1:5
iset2 <- 6:10
r <- romer(iset=list(iset1=iset1,iset2=iset2),y=y,design=design,contrast=2,nrot=99)
r
topRomer(r,alt="up")
topRomer(r,alt="down")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-2-25 23:26:17
