see.genes(maSigPro)
see.genes()所属R语言包：maSigPro
Wrapper function for visualization of gene expression values of time course experiments
基因表达的可视化的包装函数值随着时间的推移实验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function provides visualisation tools for gene expression values in a time course experiment. The function first calls the heatmap function for a general overview of experiment results. Next a partioning of the data is generated using a clustering method. The results of the clustering are visualized both as gene expression profiles extended along all arrays in the experiment, as provided by the plot.profiles function, and as summary expression profiles for comparison among experimental groups.
此功能提供了在时间课程实验的基因表达值的可视化工具。该函数首先调用的实验结果概述热图功能。未来的数据partioning使用聚类方法产生的。聚类结果的可视化基因表达谱延长作为由plot.profiles功能提供了实验，实验组之间的比较和总结的表达谱沿所有阵列。
用法----------Usage----------
see.genes(data, edesign = data$edesign, time.col = 1, repl.col = 2,
group.cols = c(3:ncol(edesign)), names.groups = colnames(edesign)[3:ncol(edesign)],
cluster.data = 1, groups.vector = data$groups.vector, k = 9, m = 1.45,
cluster.method = "hclust", distance = "cor", agglo.method = "ward",
show.fit = FALSE, dis = NULL, step.method = "backward", min.obs = 3,
alfa = 0.05, nvar.correction = FALSE, show.lines = TRUE, iter.max = 500,
summary.mode = "median", color.mode = "rainbow", cexlab = 1, legend = TRUE,
newX11 = TRUE, ylim = NULL, main = NULL, ...)
参数----------Arguments----------
参数：data
either matrix or a list containing the gene expression data, typically a get.siggenes object
无论是矩阵或列表中包含的基因表达数据，通常一个get.siggenes对象
参数：edesign
matrix of experimental design
实验设计矩阵
参数：time.col
column in edesign containing time values. Default is first column
edesign包含时间值的列。默认是第一列
参数：repl.col
column in edesign containing coding for replicates arrays. Default is second column
在edesign列包含编码复制阵列。默认是第二列
参数：group.cols
columns indicating the coding for each group (treatment, tissue,...) in the experiment (see details)
列指示每个实验组（治疗，组织，...）的编码（见详情）
参数：names.groups
names for experimental groups
为实验组的名称
参数：cluster.data
type of data used by the cluster algorithm (see details)
聚类算法所使用的数据类型（见详情）
参数：groups.vector
vector indicating the experimental group to which each variable belongs
向量表示每个变量所属的实验组
参数：k
number of clusters for data partioning
数字聚类partioning数据
参数：m
m parameter when "mfuzz" clustering algorithm is used. See mfuzz
m参数时"mfuzz"使用聚类算法。看到mfuzz
参数：cluster.method
clustering method for data partioning. Currently "hclust", "kmeans" and "mfuzz" are supported
聚类方法partioning数据。目前"hclust"，"kmeans"和"mfuzz"支持
参数：distance
distance measurement function for when cluster.method is hclust
距离测量功能时，cluster.method是hclust
参数：agglo.method
aggregation method used when cluster.method is hclust
聚合方法用时cluster.method是hclust
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. 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。可以要么"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 T.fitsignificance level. See T.fit
参数纠正T.fit显着性水平。看到T.fit
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：iter.max
maximum number of iterations when cluster.method is kmeans
最大迭代次数时cluster.method是kmeans
参数：summary.mode
the method PlotGroups takes to condensate expression information when more than one gene is present in the data. Possible values are "representative" and "median"
方法PlotGroups需要凝表达的信息，当一个以上的基因是目前在数据。可能的值是"representative"和"median"
参数：color.mode
color scale for plotting profiles. Can be either "rainblow" or "gray"
绘制剖面的颜色规模。可以要么"rainblow"或"gray"
参数：cexlab
graphical parameter maginfication to be used for x labels in plotting functions
X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：main
plot title
图标题
参数：ylim
range of the y axis to be used by PlotProfiles and PlotGroups
y轴的范围内被使用PlotProfiles和PlotGroups
参数：newX11
when TRUE, plot each type of plot in a diferent graphical device
TRUE，绘制diferent图形设备在每个类型的图
参数：...
other graphical function argument
其他图形函数的参数
Details
详情----------Details----------
Data can be provided either as a single data matrix of expression values, or a get.siggenes object. In the later case the other argument of the fuction can be taken directly from data.
数据可以作为单个数据矩阵表达式的值，或get.siggenes对象提供。其他参数的作用探讨在后一种情况下，可以采取直接从data。
Data clustering can be done on the basis of either the original expression values, the regression coefficients, or the t.scores. In case data is a get.siggenes object, this is given by providing the element names of the list c("sig.profiles","coefficients","t.score") of their list position (1,2 or 3).
原始表达式的值，回归系数，或t.scores的基础上，可以完成数据的聚类。 data是get.siggenes对象的情况下，这是通过提供的列表中的元素名称c("sig.profiles","coefficients","t.score")他们的列表中的位置（1,2或3）。
值----------Value----------
Experiment wide gene profiles and by group profiles plots are generated for each data cluster in the graphical device.
实验的全基因资料组剖面图生成的数据聚类中的每个图形设备。
参数：cut
vector indicating gene partioning into clusters
矢量显示基因簇partioning
参数：c.algo.used
clustering algorith used for data partioning
聚类的混合进化计算用于数据partioning
参数：groups
groups matrix used for plotting functions
用于绘图功能组矩阵
作者（S）----------Author(s)----------
Ana Conesa, aconesa@ivia.es; Maria Jose Nueda, mj.nueda@ua.es
参考文献----------References----------
maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102
参见----------See Also----------
PlotProfiles, PlotGroups
PlotProfiles，PlotGroups
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 270 flat profiles[＃创建270平剖面]
flat <- tc.GENE(n = 270, r = 3)
## Create 10 genes with profile differences between Ctl and Tr1 groups[＃创建10个基因与CTL和TR1组之间的轮廓差异]
twodiff <- tc.GENE (n = 10, r = 3, b2 = 0.5, c2 = 1.3)
## 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]
threediff <- tc.GENE(n = 10, r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
## Create 10 genes with profile differences between Ctl and Tr2 and different variance[＃创建10个基因与CTL和TR2和不同方差之间的轮廓差异]
vardiff <- tc.GENE(n = 10, r = 3, a3 = 0.7, b3 = 1, c3 = 1.2, var32 = 0.03, var33 = 0.03)
## Create dataset[＃创建数据集]
tc.DATA <- rbind(flat, twodiff, threediff, vardiff)
rownames(tc.DATA) <- paste("feature", c(1:300), sep = "")
colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")
tc.DATA [sample(c(1 300*36)), 300)] <- NA # introduce missing values[引进缺失值]
#### 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)
Control <- c(rep(1, 9), rep(0, 27))
Treat1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
Treat2 <- c(rep(0, 18), rep(1, 9), rep(0,9))
Treat3 <- c(rep(0, 27), rep(1, 9))
edesign <- cbind(Time, Replicates, Control, Treat1, Treat2, Treat3)
rownames(edesign) <- paste("Array", c(1:36), sep = "")
see.genes(tc.DATA, edesign = edesign, k = 4, main = "Time Course")
# This will show the regression fit curve[这将显示回归拟合曲线]
dise <- make.design.matrix(edesign)
see.genes(tc.DATA, edesign = edesign, k = 4, main = "Time Course", show.fit = TRUE,
dis = dise$dis, groups.vector = dise$groups.vector, distance = "euclidean")
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 00:24:55
