cia(made4)
cia()所属R语言包：made4
Coinertia analysis: Explore the covariance between two datasets
coinertia分析：探索两个数据集之间的协方差
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs CIA on two datasets as described by Culhane et al., 2003. Used for meta-analysis
执行两个Culhane等描述的数据集，2003年中央情报局。用于荟萃分析
用法----------Usage----------
cia(df1, df2, cia.nf=2, cia.scan=FALSE, nsc=TRUE,...)
## S3 method for class 'cia'
plot(x, nlab = 10, axis1 = 1, axis2 = 2, genecol = "gray25",
genelabels1 = rownames(ciares$co), genelabels2 = rownames(ciares$li), ...)
参数----------Arguments----------
参数：df1
The first dataset. A matrix, data.frame, ExpressionSet or marrayRaw-class. If the input is gene expression data in a matrix or data.frame. The rows and columns are expected to contain the variables (genes) and cases (array samples) respectively.
第一个数据集。一个matrix，data.frame，ExpressionSet或marrayRaw-class。如果输入的是一个matrix或data.frame的基因表达数据。预计包含的变量（基因）和例（阵列样品）分别行和列。
参数：df2
The second dataset. A matrix, data.frame, ExpressionSet or marrayRaw-class. If the input is gene expression data in a matrix or data.frame. The rows and columns are expected to contain the variables (genes) and cases (array samples) respectively.
第二个数据集。一个matrix，data.frame，ExpressionSet或marrayRaw-class。如果输入的是一个matrix或data.frame的基因表达数据。预计包含的变量（基因）和例（阵列样品）分别行和列。
参数：cia.nf
Integer indicating the number of coinertia analysis axes to be saved. Default value is 2.
整数，表示要保存的一些coinertia分析轴。默认值是2。
参数：cia.scan
Logical indicating whether the coinertia analysis eigenvalue (scree) plot should be shown so that the number of axes, cia.nf can be selected interactively. Default value is FALSE.
逻辑表示的coinertia分析特征值（卵石）图是否应显示轴数，cia.nf可以选择交互。默认值是FALSE。
参数：nsc
A logical indicating whether coinertia analysis should be performed using two non-symmetric correspondence analyses dudi.nsc. The default=TRUE is highly recommended. If FALSE, COA dudi.coa will be performed on df1, and row weighted COA dudi.rwcoa will be performed on df2 using the row weights from df1.
一个逻辑指示是否应使用两个非对称通信分析coinertia分析dudi.nsc。默认= true时，强烈建议。如果为FALSE，农委会dudi.coa将上DF1，行加权农委会dudi.rwcoa将行权使用DF1 DF2执行。
参数：x
An object of class cia, containing the CIA projected coordinates to be plotted.
一个对象类cia，包含中央情报局投影坐标绘制。
参数：nlab
Numeric. An integer indicating the number of variables (genes) to be labelled on plots.
数字。一个整数，指示变量（基因）的数量必须标明图。
参数：axis1
Integer, the column number for the x-axis. The default is 1.
整数，列数为x轴。默认值是1。
参数：axis2
Integer, the column number for the y-axis. The default is 2.
整数，列数为y轴。默认是2。
参数：genecol
Character, the colour of genes (variables). The default is "gray25".
字符，颜色的基因（变量）。默认是“gray25”。
参数：genelabels1, genelabels2
A vector of variables labels, by default the row.names of each input matrix df1, and df2 are used.
变量标签向量，默认情况下，每个输入的row.names矩阵DF1，DF2用于。
参数：...
further arguments passed to or from other methods.
通过进一步的论据或其他方法。
Details
详情----------Details----------
CIA has been successfully applied to the cross-platform comparison (meta-analysis) of microarray gene expression datasets (Culhane et al., 2003). Please refer to this paper and the vignette for help in interpretation of the output from CIA.
中央情报局已成功地应用于微阵列基因表达数据集（Culhane等，2003）的跨平台比较（Meta分析）。请参阅本文在从中央情报局输出的解释帮助的小插曲。
Co-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships in multiple datasets which contain the same samples. That is the rows or columns of the matrix have to be weighted similarly and thus must be "matchable". In cia, it is assumed that the analysis is being performed on the microarray cases, and thus the columns will be matched between the 2 datasets. Thus please ensure that the order of cases (the columns) in df1 and df2 are equivalent before performing CIA.
有限公司惯性分析（CIA）是一个多元的方法，确定趋势或合作关系，在多个数据集，其中包含相同的样本。这是加权矩阵的行或列有类似，因此必须“匹配”。 在cia，它假定正在对芯片的情况下进行分析，从而列将于2集之间的匹配。因此，请确保的情况下，为了在df1和df2（列）前执行中情局的等效。
CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses.
同时，中情局发现，从最相似的数据集，祝（降维图）。它通过寻找最大方差的两个数据集的连续轴。中情局可以应用到数据集变量（基因）的数量远远超过样本数（阵列）是微阵列分析的情况下。
cia calls coinertia in the ADE4 package. For more information on coinertia analysis please refer to coinertia and several recent reviews (see below).
cia要求coinertia在ADE4封装。如需coinertia分析的详细信息，请参阅coinertia和几个最近的评论（见下文）。
In the paper by Culhane et al., 2003, the datasets df1 and df2 are transformed using COA and Row weighted COA respectively, before coinertia analysis. It is now recommended to perform non symmetric correspondence analysis (NSC) rather than correspondence analysis (COA) on both datasets.
在Culhane等人，2003年的文件，数据集df1和df2 COA和加权农委会分别行，使用前coinertia分析转化。现在推荐两个数据集上执行非对称对应分析（NSC）的，而不是对应分析（农委会）。
The RV coefficient
的RV系数
In the results, in the object cia returned by the analysis, \$coinertia\$RV gives the RV coefficient. This is a measure of global similarity between the datasets, and is a number between 0 and 1. The closer it is to 1 the greater the global similarity between the two datasets.
结果，在对象cia通过分析返回，\ $ coinertia \ $房车给人的RV系数。这是一个全球数据集之间的相似性措施，是一个介于0和1的数量。这是越接近1更大的全球两个数据集之间的相似性。
Plotting and visualising cia results
绘图和可视化中央情报局结果
plot.cia draws 3 plots.
plot.cia投篮3图。
The first plot uses S.match.col to plots the projection (normalised scores \$mY and \$mX) of the samples from each dataset onto the one space. Cases (microarray samples) from one dataset are represented by circles, and cases from the second dataset are represented by arrow tips. Each circle and arrow is joined by a line, where the length of the line is proportional to the divergence between the gene expression profiles of that sample in the two datasets. A short line shows good agreement between the two datasets.
第一图使用S.match.col图样本的投影，每到一个空间数据集（归分数\ $ \ $ MX）。从一个数据集的情况下（芯片样品）由各界代表，和从第二个数据集的情况下代表箭头提示。每个圆圈和箭头加入一条线，线的长度成正比，在两个数据集之间的基因表达谱，样本的分歧。短行显示两个数据集之间的良好的协议。
The second two plots call plot.genes are show the projection of the variables (genes, \$li and \$co) from each dataset in the new space. It is important to note both the direction of project of Variables (genes) and cases (microarray samples). Variables and cases that are projected in the same direction from the origin have a positive correlation (ie those genes are upregulated in those microarray samples)
第二个两图称之为plot.genes显示在新的空间数据集从每个变量的预测（基因，\ $李和\ $合作）。重要的是要注意方向的变量（基因）的项目和案例（芯片样品）。预测的变量，在同一方向从源头情况呈正相关（即这些基因上调在这些芯片样品）
Please refer to the help on bga for further discussion on graphing and visualisation functions in MADE4.
bga上在MADE4制图和可视化功能的进一步讨论，请参阅帮助。
值----------Value----------
An object of the class cia which contains a list of length 4.
类cia其中的一个对象包含一个列表长度为4。
参数：call
list of input arguments, df1 and df2
输入参数，df1和df2列表
参数：coinertia
A object of class "coinertia", sub-class dudi. See coinertia
一类的“coinertia”，子类dudi对象。看到coinertia
参数：coa1
Returns an object of class "coa" or "nsc", with sub-class dudi. See dudi.coa or dudi.nsc
返回一个对象类“农委会”或“国科会”，与子类dudi。看到dudi.coa或dudi.nsc
参数：coa2
Returns an object of class "coa" or "nsc", with sub-class dudi. See dudi.coa or dudi.nsc
返回一个对象类“农委会”或“国科会”，与子类dudi。看到dudi.coa或dudi.nsc
作者（S）----------Author(s)----------
Aedin Culhane
参考文献----------References----------
参见----------See Also----------
See also coinertia, plot.cia
还可以看coinertia，plot.cia
举例----------Examples----------
data(NCI60)
print("This will take a few minutes, please wait...")
if (require(ade4, quiet = TRUE)) {
# Example data are "G1_Ross_1375.txt" and "G5_Affy_1517.txt"[例如数据的“G1_Ross_1375.txt”和“G5_Affy_1517.txt”]
coin <- cia(NCI60$Ross, NCI60$Affy)
}
coin
# ciares$RV will give the RV-coefficient, the greater (scale 0-1) the better [ciares元房车将提供更好RV系数，更大（0-1级）]
cat(paste("The RV coefficient is a measure of global similarity between the datasets.\n",
"The two datasets analysed are very similar. ",
"The RV coefficient of this coinertia analysis is: ", coin$coinertia$RV,"\n", sep= ""))
plot(coin)
plot(coin, classvec=NCI60$classes[,2], clab=0, cpoint=3)
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 00:00:36
