ExportEV(ExpressionView)
ExportEV()所属R语言包：ExpressionView
Export an ExpressionView file
导出ExpressionView文件
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Exports the biclusters identified in gene expression data with all the relevant biological data to an XML file that can be read
出口在基因表达数据与所有相关的生物数据可以读取XML文件确定的biclusters
用法----------Usage----------
## S4 method for signature 'ISAModules'
ExportEV(biclusters, eset,
order=OrderEV(biclusters), filename=file.choose(),
norm=c("sample", "feature", "raw", "x", "y"), cutoff=0.95,
description=NULL, GO, KEGG, ...)
## S4 method for signature 'Biclust'
ExportEV(biclusters, eset, order, filename, norm,
cutoff, description, ...)
## S4 method for signature 'list'
ExportEV(biclusters, eset, order=OrderEV(biclusters),
filename=file.choose(),
norm=c("sample", "feature", "raw", "x", "y"),
cutoff=0.95, description=NULL, ...)
参数----------Arguments----------
参数：biclusters
An ISAModules object, a Biclust object, or a named list, the last one possibly coming from the isa2 package.
ISAModules对象，Biclust对象，或一个命名的名单，最后一个可能来自isa2包。
参数：eset
A ExpressionSet object containing the gene expression data. Please see below how to use this function on other kind of data.
一个ExpressionSet对象，其中包含的基因表达数据。请参考下面如何使用此功能，对其他类型的数据。
参数：order
A named list (result of the OrderEV function) containing the optimal order. If not specified, an ordering with the default parameters is performed.
命名列表（OrderEV函数的结果），包含最佳秩序。如果没有指定，默认参数的顺序进行。
参数：filename
The filename of the output file. If not specified, the file is selected via the user interface.
输出文件的文件名。如果没有指定，该文件是通过用户界面的选择。
参数：norm
The normalization of the gene expression data. The isa.normalize function can normalize (zero mean and unit variance) the data with respect to the genes or the samples. Possible values: "feature", "sample" and "raw". "x" is the same as "feature" and "y" is the same as "sample". The default value is "sample".
基因表达数据的标准化。 isa.normalize功能标准化（零均值和单位方差）与有关的基因或样本的数据。可能的值：feature，sample和raw。 x是一样的feature和y是sample一样。默认值是sample。
参数：cutoff
The cutoff for the coloring is a value between 0 and 1. It represents the fraction of data points taken into account for the density plots. The default value is 0.95, i.e., the extrema of the coloring are chosen in such a way that 95% of the data points can be represented.
着色截止是一个介于0和1之间的值。它代表考虑到数据点密度图的一小部分。默认值是0.95，即着色极值选择在这样一个95％的数据点，可以代表的方式。
参数：description
A named list containing an alternative description of the data. By default, the metadata is extracted from eset. Please see below how to assemble the data description if you are dealing with data other than gene expression.
命名的列表，其中包含的数据替代描述。默认情况下，从eset元数据提取。请查看下面的如何组装的数据说明，如果你比其他基因的表达与数据处理。
参数：GO
A list of three GOListHyperGResult objects, containing the enrichment calculation results for the three Gene Ontology ontologies, for all modules, as returned by the ISAGO function in the eisa package. If not specified, then it is calculated automatically.
一个3GOListHyperGResult对象名单，含有丰富的三个基因本体论本体，所有模块返回ISAGOeisa包的功能，计算结果。如果没有指定，那么它会自动计算。
参数：KEGG
A GOListHyperGResult object, that contains the of the enrichment calculation results for all modules, against the KEGG pathway database, as returned by the ISAKEGG function in the eisa package. If not specified, then it is calculated automatically.
一个GOListHyperGResult对象，它包含KEGG通路数据库对所有的模块，返回ISAKEGG在的eisa包的功能，富集的计算结果。如果没有指定，那么它会自动计算。
参数：...
Additional arguments, nothing currently.
额外的参数，目前没有。
Details
详情----------Details----------
If the data is available in the form of a ExpressionSet, the ExportEV function automatically uses the metadata associated with the gene expression data. If the underlying data does not contain any annotations, you can provide them manually, by defining various items in the description
如果数据是在ExpressionSet，ExportEV自动使用功能与基因表达数据关联的元数据。如果基础数据不包含任何注释，可以提供手动定义中描述的各种物品，
作者（S）----------Author(s)----------
Andreas L黶cher
<a href="mailto:andreas.luescher@a3.epfl.ch">andreas.luescher@a3.epfl.ch</a>
参见----------See Also----------
OrderEV, LaunchEV,
OrderEV，LaunchEV
举例----------Examples----------
## Gene expression data[＃基因表达数据]
## We use the acute T-cell lymphocytic leukemia (ALL) data together with[＃我们使用的T单元急性淋巴单元白血病（ALL）的数据连同]
## the Iterative Signature Algorithm (ISA).[＃迭代签名算法（ISA）。]
## Load the package and the ALL data[＃装入包和所有数据。]
library(ExpressionView)
library(eisa)
library(ALL)
library(hgu95av2.db)
data(ALL)
## Initialize random number generator to get reproducible results[＃初始化随机数发生器，以获得可重复性的结果]
set.seed(5)
## Find biclusters (=modules)[＃查找biclusters（=模块）]
## To avoid some minutes of waiting, we just load the data[＃为了避免等待了几分钟，我们只是加载数据]
## set included in the 'eisa' package instead of[＃设置，而不是在“EISA”一揽子]
## really performing the calculation.[＃真正执行的计算。]
#modules <- ISA(ALL, thr.gene=2.7, thr.cond=1.4)[模块< - 赛（全，thr.gene = 2.7，thr.cond = 1.4）]
data(ALLModulesSmall)
modules <- ALLModulesSmall
## Realign the gene exptression matrix to optimize arrangements of[＃重组基因exptression矩阵，优化安排]
## biclusters [＃biclusters]
optimalorder <- OrderEV(modules)
## Export the data to an ExpressionView file[＃到ExpressionView文件导出数据]
## Don't forget to change the filename[＃不要忘记更改文件名]
## Not run: ExportEV(modules, ALL, optimalorder, filename="file.evf")[＃不运行：ExportEV（模块，所有，optimalorder，文件名=“file.evf”）]
## In-silico data[＃在硅片数据]
## We use insilico data together with the ISA and manually annotate the[＃我们使用insilico数据与ISA和手动注释]
## data set. Simply explore the data file with the Flash applet to[＃设置数据。简单地探索与Flash小程序的数据文件]
## figure out where the various annotations are placed.[＃找出各种注解被置于其中。]
## Load the package[＃装入包]
library(ExpressionView)
## Generate noisy in-silico data with dimensions m x n[＃生成尺寸MXN嘈杂的硅片数据]
m <- 50
n <- 500
data <- isa.in.silico(num.rows=m, num.cols=n, noise=0.1,
overlap.row=0)[[1]]
## Find biclusters (=modules)[＃查找biclusters（=模块）]
modules <- isa(data)
## Annotate the rows and columns of data set[＃注释数据集的行和列。]
rownames(data) <- paste("row", seq_len(nrow(data)))
colnames(data) <- paste("column", seq_len(ncol(data)))
## Add metadata associated with the rows of the data set[＃新增关联的元数据与数据集的行]
rowdata <- outer(1:nrow(data), 1:sample(1:20, 1), function(x, y) {
paste("row description (", x, ", ", y, ")", sep="")
})
rownames(rowdata) <- rownames(data)
colnames(rowdata) <- paste("row tag", seq_len(ncol(rowdata)))
## Add metadata associated with the columns of the data set [＃新增关联的元数据与数据集的列]
coldata <- outer(1:ncol(data), 1:sample(1:20, 1), function(x, y) {
paste("column description (", x, ", ", y, ")", sep="")
})
rownames(coldata) <- colnames(data)
colnames(coldata) <- paste("column tag", seq_len(ncol(coldata)))
## Merge the different annotations in a single list and [＃合并在一个列表中的不同注解和]
## add a few global things[＃添加一些全局的东西。]
description <- list(
experiment=list(
title="Title",
xaxislabel="x-Axis Label",
yaxislabel="y-Axis Label",
name="Author",
lab="Address",
abstract="Abstract",
url="URL",
annotation="Annotation",
organism="Organism"),
coldata=coldata,
rowdata=rowdata
)
## Realign the gene exptression matrix to optimize arrangements of[＃重组基因exptression矩阵，优化安排]
## biclusters [＃biclusters]
optimalorder <- OrderEV(modules)
## Export the data to an ExpressionView file[＃到ExpressionView文件导出数据]
## Don't forget to change the filename[＃不要忘记更改文件名]
ExportEV(modules, data, optimalorder, filename="file.evf",
description=description)
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 17:23:51
