plotMDS(limma)
plotMDS()所属R语言包：limma
Multidimensional scaling plot of microarray data
微阵列数据的多维尺度图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plot the sample relations based on MDS. Distances on the plot can be interpreted in terms of leading log2-fold-change.
绘制在MDS的样本为基础的关系。图上的距离，可以理解领导的log2倍变化。
用法----------Usage----------
## Default S3 method:[默认方法]
plotMDS(x, top=500, labels=colnames(x), col=NULL, cex=1, dim.plot=c(1,2), ndim=max(dim.plot), gene.selection="pairwise",
xlab=paste("Dimension",dim.plot[1]), ylab=paste("Dimension",dim.plot[2]), ...)
## S3 method for class 'MDS'
plotMDS(x, labels=colnames(x$distance.matrix), col=NULL, cex=1, dim.plot=x$dim.plot, xlab=paste("Dimension",dim.plot[1]), ylab=paste("Dimension",dim.plot[2]),...)
参数----------Arguments----------
参数：x
any data object which can be coerced to a matrix, such as ExpressionSet or EList.
任何数据对象可以强制转换为一个矩阵，如ExpressionSet或EList。
参数：top
number of top genes used to calculate pairwise distances.
顶级基因的数量来计算成对距离。
参数：labels
character vector of sample names or labels. If x has no column names, then defaults the index of the samples.
样品名称或标签的特征向量。 x如果有没有列名，则默认的指数样本。
参数：col
numeric or character vector of colors for the plotting characters.
数字或字符的矢量绘图字符的颜色。
参数：cex
numeric vector of plot symbol expansions.
数字向量积符号扩展。
参数：dim.plot
which two dimensions should be plotted, numeric vector of length two.
这两个方面，应绘制，长度为二的数字向量。
参数：ndim
number of dimensions in which data is to be represented
维数中的数据是代表
参数：gene.selection
character, "pairwise" to choose the top genes separately for each pairwise comparison between the samples or "common" to select the same genes for all comparisons
字符，"pairwise"选择顶端基因，分别为每个样品或"common"选择比较相同的基因之间的成对比较
参数：xlab
title for the x-axis
x轴标题
参数：ylab
title for the y-axis
y轴标题
参数：...
any other arguments are passed to plot.
任何其他的参数被传递到plot。
Details
详情----------Details----------
This function is a variation on the usual multdimensional scaling (or principle coordinate) plot, in that a distance measure particularly appropriate for the microarray context is used. The distance between each pair of samples (columns) is the root-mean-square deviation (Euclidean distance) for the top top genes. Distances on the plot can be interpreted as leading log2-fold-change, meaning the typical (root-mean-square) log2-fold-change between the samples for the genes that distinguish those samples.
这个函数是一个平常multdimensional缩放（或原则坐标）的图，在这个距离措施，特别是对芯片的情况下适当使用上的变化。每样本（列）之间的距离是顶端top基因的根均方差（欧氏距离）。图上的距离，可以解释为龙头的log2倍变化，这意味着典型的（根均方）之间的区分这些样本的基因样本log2倍的变化。
If gene.selection is "common", then the top genes are those with the largest standard deviations between samples. If gene.selection is "pairwise", then a different set of top genes is selected for each pair of samples. The pairwise feature selection may be appropriate for microarray data when different molecular pathways are relevant for distinguishing different pairs of samples.
如果gene.selection是"common"，然后在顶部的基因与标准样品之间的最大偏差。 gene.selection如果是"pairwise"，然后不同的顶级基因组选择对每个样品。成对的特征选择可能是适当的时，不同的分子途径，区分不同的样品对有关芯片数据。
See text for possible values for col and cex.
看到text可能col和cex值。
值----------Value----------
A plot is created on the current graphics device.
当前图形设备上创建一个图。
An object of class "MDS" is invisibly returned. This is a list containing the following components:
"MDS"类的对象是无形返回。这是一个列表，其中包含以下组件：
参数：distance.matrix
numeric matrix of pairwise distances between columns of x
数字矩阵成对x列之间的距离
参数：cmdscale.out
output from the function cmdscale given the distance matrix
输出功能cmdscale给定的距离矩阵
参数：dim.plot
dimensions plotted
尺寸绘制
参数：x
x-xordinates of plotted points
X-xordinates绘制点
参数：y
y-cordinates of plotted points
Y-cordinates绘制点
参数：gene.selection
gene selection method
基因选择方法
作者（S）----------Author(s)----------
Di Wu and Gordon Smyth
参见----------See Also----------
cmdscale
cmdscale
An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.
诊断功能概述在LIMMA提供给在09.Diagnostics。
举例----------Examples----------
# Simulate gene expression data for 1000 probes and 6 microarrays.[模拟1000探针和6微阵列基因表达数据。]
# Samples are in two groups[样品是在两组]
# First 50 probes are differentially expressed in second group[第50探针在第二组中差异表达]
sd <- 0.3*sqrt(4/rchisq(1000,df=4))
x <- matrix(rnorm(1000*6,sd=sd),1000,6)
rownames(x) <- paste("Gene",1:1000)
x[1:50,4:6] <- x[1:50,4:6] + 2
# without labels, indexes of samples are plotted.[无标签样本的指标绘制。]
mds <- plotMDS(x, col=c(rep("black",3), rep("red",3)) )
# or labels can be provided, here group indicators:[或标签可提供组指标：]
plotMDS(mds, col=c(rep("black",3), rep("red",3)), labels= c(rep("Grp1",3), rep("Grp2",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 2012-2-25 23:22:21
