clusterPlots(Repitools)
clusterPlots()所属R语言包：Repitools
Visualisation of tables of feature coverages.
可视化的功能覆盖表。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Takes the output of featureScores, or a modified version of it, and plots a heatmaps or lineplots representation of clustered coverages.
注意到输出featureScores，或它的修改版本，并绘制热图或lineplots聚类覆盖的代表性。
用法----------Usage----------
## S4 method for signature 'ClusteredScoresList'
clusterPlots(
scores.list, plot.ord = 1:length(scores.list), plot.type = c("heatmap", "line", "by.cluster"),
heat.bg.col = "black", summarize = c("mean", "median"), symm.scale = FALSE, cols = NULL, t.name = NULL,
verbose = TRUE, ...)
## S4 method for signature 'ScoresList'
clusterPlots(scores.list, scale = function(x) x,
cap.q = 0.95, cap.type = c("sep", "all"), all.mappable = FALSE, n.clusters = NULL,
plot.ord = 1:length(scores.list), expr = NULL, expr.name = NULL, sort.data = NULL,
sort.name = NULL, plot.type = c("heatmap", "line", "by.cluster"),
summarize = c("mean", "median"), cols = NULL, t.name = NULL, verbose = TRUE, ...)
参数----------Arguments----------
参数：scores.list
A ScoresList or ClusteredScoresList object.
一个ScoresList或ClusteredScoresList对象。
参数：scale
A function to scale all the coverages by. Default : No scaling.
函数扩展的覆盖。默认：不结垢。
参数：cap.q
The quantile of coverages above which to make any bigger coverages equal to the quantile.
位数以上，以便作出任何更大的覆盖范围内的分量等于覆盖。
参数：cap.type
If "sep", then the cap quantile is calculated and applied to each coverage matrix separately. If "all", then one cap quantile is calculated based on all of the matrices combined.
如果"sep"，然后盖位数计算，并分别应用到每个覆盖矩阵。如果"all"，然后一个上限位数计算的基础上，结合矩阵所有。
参数：all.mappable
If TRUE, then only features with all measurements not NA will be used.
如果属实，那么唯一与所有的测量功能不适用将使用。
参数：n.clusters
Number of clusters to find in the coverage data. Required.
数聚类覆盖率数据中找到。必需的。
参数：plot.ord
Order of the experiment types to plot.
实验类型，以图。
参数：expr
A vector of expression values.
一个表达式的值的向量。
参数：expr.name
A label, describing the expression data.
一个标签，描述表达数据。
参数：sort.data
A vector of values to sort the features within a cluster on.
值向量排序聚类内的功能。
参数：sort.name
Label to place under the sort.data plot.
标签置于sort.data图。
参数：plot.type
Style of plot to draw.
图绘制的风格。
参数：heat.bg.col
If a heatmap is being drawn, the background colour to plot NA values with.
如果正在制定热图，绘制北美值与背景颜色。
参数：summarize
How to summarise the score columns of each cluster. Not relevant for heatmap plot.
如何总结每个聚类的得分列。不积热图有关。
参数：symm.scale
Whether to make lineplot y-axis or heatmap intensity centred around 0. By default, all plots are not symmetrically ranged.
是否要lineplot Y轴或热图的强度约0。默认情况下，所有的图都没有对称不等。
参数：cols
The colours to use for the lines in the lineplot or intensities in the heatmap.
要使用的颜色，或在热图中的lineplot强度。
参数：t.name
Title to use above all the heatmaps or lineplots. Ignored when cluster-wise lineplots are drawn.
以上所有的热图或lineplots使用的标题。当绘制聚类明智lineplots忽略。
参数：verbose
Whether to print the progress of processing.
是否要打印的处理进度。
参数：...
Further graphical paramters passed to plot when heatmap plot is drawn, that influence how the points of the expression and sort data plots will look. If the lineplot is being drawn, parameters to influence the line styles.
通过进一步的图形参数研究plot热图图绘制时，这种影响如何表达和排序数据图点看。 ，如果lineplot正在制定，参数影响的线条样式。
Details
详情----------Details----------
A ClusteredScoresList should be created by the user, if they wish to do some custom clustering and normalisation on the coverage matrices. Otherwise, if the user is happy with k-means or PAM clustering, then the ScoresList object as output by featureScores() can be directly used. If called with a ScoresList, then the matrices for each coverage type are joined. Then the function supplied by the scale argument is used to scale the data. Next, each matrix is capped. Then each matrix is divided by its maximum value, so that the Euclidean distance between maximum reads and no reads is the same for each matrix. Lastly, either k-means or PAM clustering is performed to get the cluster membership of each feature. If there are any NAs in the scores, then PAM will be used. Otherwise, k-means is used for speed. Then, a ClusteredScoresList object is created, and used. The clusters are guaranteed to be given IDs in descending order of summarised cluster expression, if it is provided. If called with a ClusteredScoresList, no scaling or capping is done, so it is the user's responsibility to normalise the coverage matrices as they see fit, when creating the ClusteredScoresList object.
一个ClusteredScoresList应当由用户创建的，如果他们想覆盖矩阵做一些自定义的聚类和规范化。否则，如果用户是快乐的k-means或PAM聚类，然后ScoresList对象作为featureScores()输出可以直接使用。如果调用ScoresList，然后为每个覆盖类型的矩阵加入。然后由scale参数提供的功能是用来扩展数据。接下来，每个矩阵的上限。然后每个矩阵除以它的最大价值，所以，欧氏距离之间的最大读取并没有读取每个矩阵是相同的。最后，无论是执行的k-means或PAM聚类获得每个功能的聚类成员。如果有任何NAS，然后在分数PAM将使用。否则，K-means的使用速度。然后，创建一个ClusteredScoresList对象，并使用。聚类是要下降汇总聚类表达的顺序的ID，如果提供了保证。如果调用一个ClusteredScoresList，不结垢或覆盖完成，因此，它是用户的责任标准化覆盖矩阵，因为他们看到合适的，当创建ClusteredScoresList对象。
If a ClusteredScoresList object is subsetted, the original data range is saved in a private slot, so that if the user wants to plot a subset of the features, such as a certain cluster, for example, the intensity range of the heatmap, or the y-axis range of the lineplot will be the same as before subsetting.
ClusteredScoresList对象的子集，如果原始数据的范围将被保存在一个私人插槽，因此，如果用户要绘制一个功能子集，如某些聚类，例如，在强度范围热图，或Y轴的lineplot范围将前子集相同。
If expression data is given, the summarised expression level of each cluster is calculated, and the clusters are plotted in order of decreasing expression, down the page. Otherwise, they are plotted in ascending order of cluster ID. If a heatmap plot is being drawn, then a heatmap is drawn for every coverage matrix, side-by-side, and a plot of each feature's expression is put alongside the heatmaps, if provided. If additional sort vector was given, the data within clusters are sorted on this vector, then a plot of this data is made as the rightmost graph.
如果表达数据，每个聚类的概要表达水平计算，聚类绘制页，为了降低表达下降。否则，他们绘制的聚类ID升序。如果热图图正在草拟，然后每覆盖矩阵，由方方，每个功能的表达图绘制热图放在一起，如果提供的热图。如果给予额外的排序向量，聚类内的数据，在此向量排序，然后这些数据的图是由最右边的图。
The lineplot style is similar to the heatmap plot, but clusters are summarised. A grid, with as many rows as there are clusters, and as many columns as there are clusters is made, and lineplots showing the summarised scores are made in the grid. Beside the grid, a boxplot of expression is drawn for each cluster, if provided.
lineplot风格是类似的热图的图，但聚类的总结。是一个有很多聚类行，因为有很多聚类列格，汇总分数lineplots电网。旁边的电网，表达盒形图绘制的每个聚类，如果提供。
For a cluster-wise lineplot, a graph is drawn for each cluster, with the colours being the different coverage types. Because it makes sense that there will be more clusters than there are types of coverage (typically double to triple the number), the plots are not drawn side-by-side, as is the layout for the heatmaps. For this reason, sending the output to a PDF device is necessary. It is recommended to make the width of the PDF device wider than the default. Since the coverage data between different marks is not comparable, this method is inappropriate for visualising a ClusteredScoresList object if it was created by the clusterPlots scoresList method. If the user, however, can come up with a normalisation method to account for the differences that are apparent between different types (i.e. peaked vs. spread) of marks that makes the coverages meaningfully comparable, they can alter the tables, do their own clustering, and create a ClusteredScoresList object with the modified tables.
图形为聚类明智lineplot，绘制每个聚类，不同覆盖类型的颜色。会有更多的聚类比有覆盖类型（通常一倍至三倍的数量），因为它是有道理的，图不绘制端侧，是热图的布局。出于这个原因，将输出发送到一个PDF的设备是必要的。据建议，以使PDF的设备比默认更宽的宽度。由于不同标志的覆盖率数据不具可比性，这种方法是不适当的可视化ClusteredScoresList对象，如果它被由clusterPlots scoresList法创建。如果用户，但是，可以有明显的差异，不同类型的商标，使有意义的覆盖范围可比（即见顶与传播）归一化方法来了，他们可以改变表，做自己的聚类，并创建一个ClusteredScoresList修改的表的对象。
值----------Value----------
If called with a ScoresList, then a ClusteredScoresList is returned. If called with a ClusteredScoresList, then nothing is returned.
如果叫ScoresList，然后ClusteredScoresList返回。如果ClusteredScoresList调用，然后返回任何值。
作者（S）----------Author(s)----------
Dario Strbenac
参见----------See Also----------
featureScores for generating coverage matrices.
featureScores生成覆盖率矩阵。
举例----------Examples----------
data(samplesList) # Loads 'samples.list.subset'.[负载“samples.list.subset”。]
data(expr) # Loads 'expr.subset'.[负载“expr.subset”。]
data(chr21genes)
fs <- featureScores(samples.list.subset[1:2], chr21genes, up = 2000, down = 1000,
freq = 500, s.width = 500)
clusterPlots(fs, function(x) sqrt(x), n.clusters = 5, expr = as.numeric(expr.subset),
plot.type = "heatmap", pch = 19, cex = 0.5)
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 12:48:30
