PlotGroups(maSigPro)
PlotGroups()所属R语言包：maSigPro
Function for plotting gene expression profile at different experimental groups
绘制基因表达图谱的功能在不同的实验组
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function displays the gene expression profile for each experimental group in a time series gene expression experiment.
此功能在时间序列的基因表达实验显示各实验组的基因表达谱。
用法----------Usage----------
PlotGroups(data, edesign = NULL, time = edesign[,1], groups = edesign[,c(3:ncol(edesign))],
repvect = edesign[,2], show.fit = FALSE, dis = NULL, step.method = "backward",
min.obs = 2, alfa = 0.05, nvar.correction = FALSE, summary.mode = "median", show.lines = TRUE, groups.vector = NULL,
xlab = "time", cex.xaxis = 1, ylim = NULL, main = NULL, cexlab = 0.8, legend = TRUE, sub = NULL)
参数----------Arguments----------
参数：data
vector or matrix containing the gene expression data
包含的基因表达数据的向量或矩阵
参数：edesign
matrix describing experimental design. Rows must be arrays and columns experiment descriptors
矩阵描述实验设计。行必须是阵列和列实验描述
参数：time
vector indicating time assigment for each array
向量表示每个阵列的时间assigment
参数：groups
matrix indicating experimental group to which each array is assigned
矩阵表示每个阵列分配到实验组
参数：repvect
index vector indicating experimental replicates
索引向量表示实验复制
参数：show.fit
logical indicating whether regression fit curves must be plotted
逻辑说明是否必须绘制回归拟合曲线
参数：dis
regression design matrix
回归设计矩阵
参数：step.method
stepwise regression method to fit models for cluster mean profiles. It can be either "backward", "forward", "two.ways.backward" or "two.ways.forward"
逐步回归方法，适合聚类模型意味着型材。它可以是"backward"，"forward"，"two.ways.backward"或"two.ways.forward"
参数：min.obs
minimal number of observations for a gene to be included in the analysis
在分析中被列入观察基因的数量最少
参数：alfa
significance level used for variable selection in the stepwise regression
显着性水平在逐步回归变量选择
参数：nvar.correction
argument for correcting stepwise regression significance level. See T.fit
参数纠错逐步回归显着性水平。看到T.fit
参数：summary.mode
the method to condensate expression information when more than one gene is present in the data. Possible values are "representative" and "median"
凝表达信息的方法，当一个以上的基因是在目前的数据。可能的值是"representative"和"median"
参数：show.lines
logical indicating whether a line must be drawn joining plotted data points for reach group
逻辑指示行是否必须制定加盟绘制河段组数据点
参数：groups.vector
vector indicating experimental group to which each variable belongs
矢量表明实验组的每个变量都属于
参数：xlab
label for the x axis
X轴的标签
参数：cex.xaxis
graphical parameter maginfication to be used for x axis in plotting functions
X轴使用绘图功能的图形参数maginfication
参数：ylim
range of the y axis
y轴的范围
参数：main
plot main title
图主标题
参数：cexlab
graphical parameter maginfication to be used for x axis label in plotting functions
X轴标签使用绘图功能的图形参数maginfication
参数：legend
logical indicating whether legend must be added when plotting profiles
逻辑表明是否绘制剖面时，必须补充传说
参数：sub
plot subtitle
图字幕
Details
详情----------Details----------
To compute experimental groups either a edesign object must be provided, or separate values must be given for the time, repvect and groups arguments.
计算实验组必须提供无论是edesign对象，或单独的值必须为time，repvect和groups参数。
When data is a matrix, the average expression value is displayed.
当数据是一个矩阵，平均表达值显示。
When there are array replicates in the data (as indicated by repvect), values are averaged by repvect.
当阵列中的数据（如，repvect表示），值平均repvect复制。
PlotGroups plots one single expression profile for each experimental group even if there are more that one genes in the data set. The way data is condensated for this is given by summary.mode. When this argument takes the value "representative", the gene with the lowest distance to all genes in the cluster will be plotted. When the argument is "median", then median expression value is computed.
PlotGroups图各实验组的表达谱，即使有更多的基因数据集。数据此冷凝方式summary.mode。当此参数的值为"representative"，与最低的距离，聚类中的所有基因的基因将被绘制。当参数是"median"，然后表达式的值中位数计算。
When show.fit is TRUE the stepwise regression fit for the data will be computed and the regression curves will be displayed.
当show.fit是TRUE逐步回归拟合数据将计算和回归曲线将显示。
If data is a matrix of genes and summary.mode is "median", the regression fit will be computed for the median expression value.
如果数据是一个矩阵基因summary.mode是"median"，回归拟合将表达式的值中位数计算。
值----------Value----------
Plot of gene expression profiles by-group.
图的基因表达谱由组。
作者（S）----------Author(s)----------
Ana Conesa, <a href="mailto:aconesa@ivia.es">aconesa@ivia.es</a>; Maria Jose Nueda, <a href="mailto:mj.nueda@ua.es">mj.nueda@ua.es</a>
参考文献----------References----------
maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.
参见----------See Also----------
PlotProfiles
PlotProfiles
举例----------Examples----------
#### GENERATE TIME COURSE DATA[＃＃＃生成时间的课程资料]
## generate n random gene expression profiles of a data set with [＃生成N个随机设置数据基因表达谱]
## one control plus 3 treatments, 3 time points and r replicates per time point.[＃一个控制加3个疗程，3个时间点和r每时间点复制。]
tc.GENE <- function(n, r,
var11 = 0.01, var12 = 0.01,var13 = 0.01,
var21 = 0.01, var22 = 0.01, var23 =0.01,
var31 = 0.01, var32 = 0.01, var33 = 0.01,
var41 = 0.01, var42 = 0.01, var43 = 0.01,
a1 = 0, a2 = 0, a3 = 0, a4 = 0,
b1 = 0, b2 = 0, b3 = 0, b4 = 0,
c1 = 0, c2 = 0, c3 = 0, c4 = 0)
{
tc.dat <- NULL
for (i in 1:n) {
Ctl <- c(rnorm(r, a1, var11), rnorm(r, b1, var12), rnorm(r, c1, var13)) # Ctl group[CTL组]
Tr1 <- c(rnorm(r, a2, var21), rnorm(r, b2, var22), rnorm(r, c2, var23)) # Tr1 group[TR1组]
Tr2 <- c(rnorm(r, a3, var31), rnorm(r, b3, var32), rnorm(r, c3, var33)) # Tr2 group[TR2组]
Tr3 <- c(rnorm(r, a4, var41), rnorm(r, b4, var42), rnorm(r, c4, var43)) # Tr3 group[TR3组]
gene <- c(Ctl, Tr1, Tr2, Tr3)
tc.dat <- rbind(tc.dat, gene)
}
tc.dat
}
## create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups[＃创建10个基因与CTL，TR2，TR3组之间的轮廓差异]
tc.DATA <- tc.GENE(n = 10,r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
rownames(tc.DATA) <- paste("gene", c(1:10), sep = "")
colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")
#### CREATE EXPERIMENTAL DESIGN[＃＃＃创建一个实验设计]
Time <- rep(c(rep(c(1:3), each = 3)), 4)
Replicates <- rep(c(1:12), each = 3)
Ctl <- c(rep(1, 9), rep(0, 27))
Tr1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
Tr2 <- c(rep(0, 18), rep(1, 9), rep(0, 9))
Tr3 <- c(rep(0, 27), rep(1, 9))
PlotGroups (tc.DATA, time = Time, repvect = Replicates, groups = cbind(Ctl, Tr1, Tr2, Tr3))
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 00:24:28
