safe(safe)
safe()所属R语言包：safe
Significance Analysis of Function and Expression
功能和表达的意义分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs a significance analysis of function and expression (SAFE) for a given gene expression experiment and a given set of functional categories. SAFE is a two-stage permutation-based method that can be applied to a 2-sample, multi-class, simple linear regression, and other linear models. Other experimental designs can also be accommodated through user-defined functions.
执行功能和表达一个特定基因表达的实验和给定的功能类别（外管局）的意义分析。 SAFE是一个两阶段的排列为基础的方法，可应用于2样本，多类，简单的线性回归，和其他的线性模型。通过用户定义的函数，也可以容纳其他的实验设计。
用法----------Usage----------
safe(X.mat, y.vec, C.mat = NULL, platform = NULL, annotate = NULL, Pi.mat = NULL,
local = "default", global = "Wilcoxon", args.local = NULL,
args.global = list(one.sided = FALSE), error = "none", alpha = NA,
method = "permutation", min.size = 2, max.size = Inf, ...)
参数----------Arguments----------
参数：X.mat
A matrix or data.frame of expression data; each row corresponds to a gene and each column to a sample. Data can also be given as the Bioconductor class ExpressionSet. Data should be properly normalized and may not contain missing values.
表达数据矩阵或数据框，每行对应一个基因和每列一个样本。数据也可以作为“的Bioconductor类ExpressionSet。数据应妥善标准化，并可能不包含缺失值。
参数：y.vec
a numeric, integer or character vector of length ncol(X.mat) containing the response of interest. If X.mat is an ExpressionSet, y.vec can also be the name or column number of a covariate in the phenoData slot. For examples of the acceptable forms y.vec can take, see the vignette.
一个长度为向量的数字，整数或字符ncol(X.mat)包含利益的响应。如果X.mat是一个ExpressionSet，y.vec也可以在phenoData插槽的名称或列数的协变量。为可接受形式的例子y.vec可以看到的小插曲。
参数：C.mat
A matrix or data.frame containing the gene category assignments. Each column represents a category and should be named accordingly. For each column, values of 1 (TRUE) and 0 (FALSE) indicate whether the genes in the corresponding rows of X.mat are contained in the category. This can also be a list containing a sparse matrix and dimnames as created by getCmatrix
矩阵或数据框包含的基因类别分配。每一列代表一个类别，并应据此命名。对于每一列，值1（TRUE）和0（FALSE）显示在相应的行的基因是否X.mat都在类别中。这也可以是一个列表，其中包含一个稀疏矩阵和作为getCmatrix创建dimnames
参数：platform
If C.mat is unspecified, a character string of a Bioconductor annotation package can be used to build gene categories. See vignette for details and examples.
C.mat如果是不确定的，Bioconductor注解包的字符串可以用来建立基因类别。看到的细节和例子的小插曲。
参数：annotate
If C.mat is unspecified, a character string to specify the type of gene categories to build from annotation packages. "GO.MF", "GO.BP", "GO.CC", and "GO.ALL" (default) specify one or all Gene Ontologies. "KEGG" specifies pathways, and " FAM" homologous families from the respective sources.
C.mat如果是不确定的，一个字符串指定类型的基因类别建立注解包。 “GO.MF”，“GO.BP”，“GO.CC”，“GO.ALL”（默认）指定一个或多个基因本体。 “KEGG”规定的途径，“PFAM”同源从各自的来源的家庭。
参数：Pi.mat
Either a matrix or data.frame containing the permutations, or an integer. See getPImatrix for the acceptable form of a matrix or data.frame. If Pi.mat is an integer, then safe will automatically generate as many random permutations of X.mat.
无论是矩阵或数据框包含的排列，或一个整数。看到getPImatrix矩阵或数据框可以接受的形式。 Pi.mat如果是一个整数，那么safe会自动产生尽可能多的X.mat随机排列。
参数：local
Specifies the gene-specific statistic from the following options: "t.Student", "t.Welch" and "t.SAM" for 2-sample designs, "f.ANOVA" for 1-way ANOVAs, "t.LM" for simple linear regressions, and "z.COXPH" for a Cox proportional hazards survival model. "default" will choose between "t.Student" and "f.ANOVA", based on the form of y.vec. User-defined local statistics can also be used; details are provided in the vignette.
指定统计：从以下选项中的特定基因“t.Student”，“t.Welch”和“t.SAM”2样品设计，“f.ANOVA”1路方差分析，“ ;简单线性回归，“z.COXPH Cox比例风险生存模型”t.LM“。 “默认”将选择之间的“t.Student”和“f.ANOVA”，上y.vec形式的基础。用户定义的本地统计也可以使用;细节中的小插曲。
参数：global
Specifies the global statistic for a gene categories. By default, the Wilcoxon rank sum ("Wilcoxon") is used. Else, a Fisher's Exact test statistic ("Fisher") based on the hypergeometric dist'n, a chi-squared type Pearson's test ("earson") or t-test of average difference ("AveDiff") is available. User-defined global statistics can also be implemented.
指定的基因类别的全球统计。默认情况下，使用秩（“秩”）。否则，费希尔的精确测试统计（“雪”）超几何distn的，卡平方型皮尔逊的测试（“培”）或平均差异的t检验（“AveDiff”）。用户定义的全局统计信息，也可以实现。
参数：args.local
An optional list to be passed to user-defined local statistics that require additional arguments. By default args.local = NULL.
可选列表被传递到用户定义的，需要额外的参数的本地统计。默认情况下args.local = NULL。
参数：args.global
An optional list to be passed to global statistics that require additional arguments. For two-sided local statistics, args.global = list(one.sided=F) allows bi-directional differential expression to be considered.
要传递给全球统计，需要额外的参数可选列表。对于双面当地统计，args.global=列表（one.sided = F）的，允许被视为双向的差异表达。
参数：error
Specifies the method for computing error rate estimates. "FDR.YB" computes the Yekutieli-Benjamini FDR estimate, "FWER.WY" computes the Westfall-Young FWER estimate. A Bonferroni, ("FWER.Bonf"), Holm's step-up ("FWER.Holm"), and Benjamini-Hochberg step down ("FDR.BH") adjustment can also be specified. By default ("none") no error rates are computed.
指定计算错误率估计的方法。 “FDR.YB”计算的Yekutieli BenjaminiFDR估计，的“FWER.WY”计算的荒野年轻FWER，估计。还可以指定一个邦弗朗尼，（“FWER.Bonf”），霍尔姆的一步（“FWER.Holm”），和Benjamini-Hochberg下台（“FDR.BH”）调整。 （“无”）默认情况下，没有错误率计算。
参数：alpha
Allows the user to define the criterion for significance. By default, alpha will be 0.05 for nominal p-values (error = "none" ), and 0.1 otherwise.
允许用户定义标准的意义。默认情况下，α会名义p值（error=“无”），0.05和0.1。
参数：method
Type of hypothesis test can be specified as "permutation", "bootstrap.t", and "bootstrap.q". See vignette for details
假设检验的类型，可以被指定为“置换”，“bootstrap.t”，“bootstrap.q”。查看详情插曲
参数：min.size
Optional minimum category size to be considered.
可选的最低类别的大小要考虑的。
参数：max.size
Optional maximum category size to be considered.
可选的最大的品类规模加以考虑。
参数：...
Allows arguments from version 1.0 to be ignored
允许从版本1.0论据被忽略
Details
详情----------Details----------
safe utilizes a general framework for testing differential expression across gene categories that allows it to be used in various experimental designs. Through structured resampling of the data, safe accounts for the unknown correlation among genes, and enables proper estimation of error rates when testing multiple categories. safe also provides statistics and empirical p-values for the gene-specific differential expression.
safe利用一个测试跨越差异表达的基因类，允许它被用于各种实验设计的总体框架。通过结构化的数据重采样，safe账户之间未知的相关基因，并允许错误率的正确估计，在测试多个类别。 safe还提供统计信息和经验p值的特定基因的差异表达。
值----------Value----------
The function returns an object of class SAFE. See help for SAFE-class for more details.
该函数返回一个对象的类SAFE。 SAFE-class更多详情，请参阅帮助。
作者（S）----------Author(s)----------
William T. Barry: <a href="mailto:bill.barry@duke.edu">bill.barry@duke.edu</a>
参考文献----------References----------
of functional categories in gene expression studies: a structured permutation approach, Bioinformatics 21(9) 1943–1949.
<h3>See Also</h3> <code>safeplot</code>, <code>getCmatrix</code>,
举例----------Examples----------
## Simulate a dataset with 1000 genes and 20 arrays in a 2-sample design.[＃模拟2样本设计有1000个基因和20阵列的数据集。]
## The top 100 genes will be differentially expressed at varying levels[＃前100个基因，将在不同程度的差异表达]
g.alt <- 100
g.null <- 900
n <- 20
data<-matrix(rnorm(n*(g.alt+g.null)),g.alt+g.null,n)
data[1:g.alt,1 n/2)] <- data[1:g.alt,1n/2)] +
seq(2,2/g.alt,length=g.alt)
dimnames(data) <- list(c(paste("Alt",1:g.alt),
paste("Null",1:g.null)),
paste("Array",1:n))
## A treatment vector [＃A治疗向量]
trt <- rep(c("Trt","Ctr"),each=n/2)
## 2 alt. categories and 18 null categories of size 50[＃2 ALT。类别和18类50大小的空]
C.matrix <- kronecker(diag(20),rep(1,50))
dimnames(C.matrix) <- list(dimnames(data)[[1]],
c(paste("TrueCat",1:2),paste("NullCat",1:18)))
dim(C.matrix)
results <- safe(data,trt,C.matrix,Pi.mat = 100)
results
## SAFE-plot made for the first category[＃安全第一类的图]
if (interactive()) {
safeplot(results,"TrueCat 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 2012-2-26 13:45:13
