gsealmPerm(GSEAlm)
gsealmPerm()所属R语言包：GSEAlm
Nonparametric inference for linear models in Gene-Set-Enrichment
基因集富集线性模型的非参数推断
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Provides permutation-based p-values for a main effect at the gene-set level, potentially adjusting for the effect of other variables via a linear model. This is a generalization and upgrade of gseattperm.
提供排列为基础的p值在基因组水平的主要影响，调整可能对其他变量通过线性模型的影响。这是一个概括和升级gseattperm。
用法----------Usage----------
gsealmPerm(eSet, formula = "", mat, nperm, na.rm = TRUE,pooled=FALSE,detailed=FALSE,...)
参数----------Arguments----------
参数：eSet
An ExpressionSet object.
ExpressionSet对象。
参数：formula
An object of class formula (or one that can be coerced to that class), specifying only the right-hand side starting with the '~' symbol. The LHS is automatically set as the expression levels provided in eSet. The names of all predictors must exist in the phenotypic data of eSet. See more below in "Details".
一个类的对象formula（或一个可以强制该级），指定只有右侧的~符号开始。 LHS是自动设置为eSet提供的表达水平。所有预测的名称必须存在于eSet表型数据。在下面看到更多的“细节”。
参数：mat
A 0/1 incidence matrix with each row representing a gene set and each column representing a gene. A 1 indicates membership of a gene in a gene set.
一个0/1的发病率矩阵每行代表一个基因组，每一列代表一个基因。 1表示一个基因在基因组的成员。
参数：nperm
Number of permutations used to simulate the reference null distribution.
用来模拟参考空分布排列。
参数：na.rm
Should missing observations be ignored? (passed on to lmPerGene)
应该失踪的意见被忽视？ （通过lmPerGene）
参数：pooled
Should variance be pooled across all genes? (passed on to lmPerGene)
应汇集所有基因变异？ （通过lmPerGene）
参数：detailed
Would you like a detailed output, or just the p-values? Defaults to FALSE for back-compatibility.
您想详细的输出，或只是p值吗？默认为FALSE向后兼容性。
参数：...
Additional parameters passed on to GSNormalize.
额外的参数传递到GSNormalize。
Details
详情----------Details----------
If a formula is provided, the permutation test permutes sample (i.e. column) labels, so essentially the effect is compared with the null distribution of effects for *each particular gene-set separately*. This neutralizes the impact of intra-sample correlations. If the formula contains two or more covariates, the effect of interest must be the first one in the formula. This effect's covariate values are permuted within each subgroup defined by identical values on all other covariates. This means, that the other covariates *must* be discrete, otherwise the analysis is meaningless. The effect of interest is the only one that can be continuous.
如果提供了一个公式，排列测试置换样品标签（即列），所以基本上效果相比，空分布的影响*每一个特定的基因组，分别*。这抵消样本内相关性的影响。如果配方中含有两个或两个以上的变项，利益的影响，必须在公式中的第一个。这种效应的协变量的值是所有其他变相同的值定义每个分组内置换。这意味着，其他变*必须*是离散的，否则的分析是没有意义的。利益的影响是唯一一个可以是连续的。
If a formula is *not* provided, a row-permutation test is performed on average expression levels. This test examines whether each gene-set is differentially expressed (on the average), compared with a permutation baseline of random gene-sets of the same size.
如果一个公式是*不*提供，行排列的测试平均表达水平。这个测试是检查是否每个基因组的差异表达（平均），与一个同样大小的随机基因组排列基线相比。
The p-values have now been corrected to reflect the accepted statistical approach, i.e. that the observed data is considered part of the permutation distribution under the null. Hence, p-values of zero are impossible from now on. This is hard-coded.
p值现在已经得到纠正，以反映公认的统计方法，即观测到的数据被认为是空下的排列分布的一部分。因此，P-值为零，是不可能从现在开始。这是硬编码。
值----------Value----------
If detailed=FALSE, A matrix with the same number of rows as mat and two columns, "Lower" and "Upper". The "Lower" ("Upper") column gives the probability of seeing a t-statistic smaller or equal (larger or equal) to the observed. If 'mat' had row names, so will the output.
如果detailed=FALSE，矩阵具有相同数量的行mat和两列，“低”和“上层”。 “低”（“上限”）列给出看到的t-统计量小于或等于（大于或等于）观测到的概率。如果垫“行名，因此将输出。
If detailed=TRUE, A list with components:
如果detailed=TRUE，与组件列表：
参数：pvalues
The above-mentioned, two-column p-value matrix.
上述两列p值矩阵。
参数：lmfit
The lmPerGene object generated by fitting the true model matrix (without permutations).
lmPerGene拟合真实的模型矩阵（无排列）生成的对象。
参数：stats
The observed statistics generated via the true model; i.e., the ones for which the p-values are calculated.
通过真实模型生成的观测统计，即，其中P-值计算的。
参数：perms
The full matrix of permutation statistics, of dimension nrow(mat) x nperm.
mat的排列统计的矩阵尺寸NROW，（）Xnperm。
警告----------Warnings ----------
1. Inference is *only* for the first term in the model. If you want inference for more terms, re-run the function on the same model, changing order of terms each time.
1。推理是模型中的第一个任期内*仅*。如果你想要更多的条件推断，再上运行的同一型号的功能，改变每次条款顺序。
2. To repeat: the adjusting covariates (all terms except the first) have to be discrete. Adding a continuous covariate with unique values for most samples, may result in an infinite loop. However, you *can* put a continuous covariate as your first term.
2。重复：调节变项（除第一所有条款）是离散的。加入了独特的价值观与大多数样品的连续协变量，可能会导致一个无限循环。然而，你可以*把你的第一个任期内连续协变量。
注意----------Note----------
This function is a generic template for GSEA permutation tests. The particular type of GSEA statistic used is determined by GSNormalize, which is called by this function. Permutations are generated via repeated calls to lmPerGene.
这个函数是一个GSEA排列测试的通用模板。的GSEA使用统计的特定类型确定GSNormalize，这是由这个函数调用。通过反复调用lmPerGene生成的排列。
作者（S）----------Author(s)----------
Assaf Oron
参见----------See Also----------
gseattperm,GSNormalize, lmPerGene. The GlobalAncova package provides a generic $F$-test for model selection, while gsealmPerm can be
gseattperm，GSNormalize，lmPerGene。 GlobalAncova包提供了一个通用的元模型选择F $测试，而gsealmPerm可以
举例----------Examples----------
data(sample.ExpressionSet)
### Generating random pseudo-gene-sets[＃生成随机伪基因组]
fauxGS=matrix(sample(c(0,1),size=50000,replace=TRUE,prob=c(.9,.1)),nrow=100)
### inference for sex: sex is first term[＃推断性别：性别是第一个任期]
sexPvals=gsealmPerm(sample.ExpressionSet,~sex+type,mat=fauxGS,nperm=40)
### inference for type: type is first term[＃类型：类型推断是第一个任期]
typePvals=gsealmPerm(sample.ExpressionSet,~type+sex,mat=fauxGS,nperm=40,removeShift=TRUE)
### plotting the p-values; note that the effect direction depends upon[＃策划的p值;注意，影响方向取决于]
### factor level order (defaults to alphabetical)[＃因子水平的顺序（默认以英文字母）]
layout(t(1:2))
### Sex p-values are center-heavy, typical when the effect is dominated[＃＃性别p值中心重，典型的效应占主导地位时]
### by another effect[＃另一个效果]
hist(sexPvals[,2],10,main="Sex Effect p-values",xlab="p-values for Male minus Female",xlim=c(0,1))
### The dominating effect is type, where there is a baseline shift in[＃的主导作用是类型，其中有一个基线漂移]
### favor of controls[＃有利于控制]
hist(typePvals[,1],10,main="Type Effect p-values",xlab="p-values for Case minus Control",xlim=c(0,1))
############[＃＃＃＃＃＃＃＃＃＃＃]
### Modeling type again - and now we add a baseline-shift removal (the 'removeShift' argument passed on to 'GSNormalize')[＃建模类型 - 现在我们添加了一个去除基线移（“removeShift参数传递给的”GSNormalize“）]
typePvals1=gsealmPerm(sample.ExpressionSet,~type+sex,mat=fauxGS,nperm=40,removeShift=TRUE)
### Modeling type again - and now the shift removal is by mean instead[＃＃建模类型 - 和现在的转变去除的意思，而不是]
### of the default median[＃默认位数]
typePvals2=gsealmPerm(sample.ExpressionSet,~type+sex,mat=fauxGS,nperm=40,removeShift=TRUE,removeStat=mean)
### Now notice the differences between the 3 versions! This is a weird[＃现在发现3个版本之间的差异！这是一个奇怪的]
### dataset indeed; it's also important to undrestand which research[＃集确实也很重要undrestand其中研究]
### question you are trying to answer  [＃质疑你正试图回答]
hist(typePvals1[,1],10,main="Type Effect p-values",xlab="p-values for Case minus Control",xlim=c(0,1))
hist(typePvals2[,1],10,main="Type Effect p-values",xlab="p-values for Case minus Control",xlim=c(0,1))
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 2012-2-25 21:18:09
