sim.plot.zscore.heatmap(SIM)
sim.plot.zscore.heatmap()所属R语言包：SIM
Association heatmap from z-scores
从Z-分数协会热图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Produces an association heatmap that shows the association (standardized influence) of each independent feature (expression measurement) with each dependent feature (copy number measurement). A P-value bar on the left indicates test signficance. A color bar on top indicates genes with mean z-scores across the signficant copy number probes above a set threshold. A summary of the copy number data helps to identify what copy number alterations are present in a region of association with expression. Positive association can mean copy number gain and increased expression, or deletion and decreased expression. The heatmaps can also be used in an exploratory analysis, looking for very local effects of copy number changes (usually small amplifications) on gene expression, that do not lead to a significant test result.
产生关联的热图，显示每个独立的功能（表达测量）协会（标准化的影响），每名受供养的功能（拷贝数测量）。 P值在左侧栏显示测试建设的重大意义。在上面的彩条表示平均z分数跨越设定的阈值以上的signficant副本数量探针基因。一个拷贝数的汇总数据有助于确定什么复制数量的改变是在表达了与区域。正相关关系可能意味着拷贝数增益和表达增加，或删除，并表达下降。的热图也可以被用来在一个探索性分析，寻找对基因表达的拷贝数变化（通常是小片段），不会导致一个重要的测试结果非常当地的影响。
用法----------Usage----------
sim.plot.zscore.heatmap(input.regions = "all chrs",
input.region.indep = NULL,
method = c("full", "smooth", "window", "overlap"),
adjust = ~1,
significance = 0.2,
z.threshold = 3,
colRamp = colorRampPalette(c("red", "black", "green")),
add.colRamp = colorRampPalette(c("blue", "black", "yellow"))(7),
show.names.indep = FALSE,
show.names.dep = FALSE,
adjust.method = "BY",
scale,
add.scale,
add.plot = c("smooth", "none", "heatmap"),
smooth.lambda = 2,
pdf = TRUE,
run.name = "analysis_results",...)
参数----------Arguments----------
参数：input.regions
vector indicating the dependent regions to be analyzed. Can be defined in four ways: 1) predefined input region: insert a predefined input region, choices are: “all chrs”, “all chrs auto”, “all arms”, “all arms auto” In the predefined regions “all arms” and “all arms auto” the arms 13p, 14p, 15p, 21p and 22p are left out, because in most studies there are no or few probes in these regions. To include them, just make your own vector of arms. 2) whole chromosome(s): insert a single chromosome or a list of chromosomes as a vector: c(1, 2, 3). 3) chromosome arms: insert a single chromosome arm or a list of chromosome arms like c("1q", "2p", "2q"). 4) subregions of a chromosome: insert a chromosome number followed by the start and end position like "chr1:1-1000000" These regions can also be combined, e.g. c("chr1:1-1000000","2q", 3). See integrated.analysis for more information.
vector表明依赖区域进行分析。可以定义在四个方面：1) predefined input region: 插入一个预定义的输入区域，选择是：“所有CHRS”，“所有CHRS汽车”，“武器”，“所有武器的汽车”在预定区域“所有武器”和“所有自动武器”的武器，13P，14P，15P，21P和22P冷落，因为在大多数研究中，有没有在这些区域或几个探针。包括他们，才使自己的武器向量。 2) whole chromosome(s): 插入一个vector:c(1, 2, 3)的一个单一的染色体或染色体列表。 3) chromosome arms: 插入一个单一的染色体或染色体臂像c("1q", "2p", "2q")名单。 4) subregions of a chromosome: 插入一个染色体数目的开始和结束位置，如"chr1:1-1000000"这些区域也可以结合，如c("chr1:1-1000000","2q", 3)。看到integrated.analysis更多信息。
参数：input.region.indep
indicating the independent region which will be analysed in combination of the dependent region. Only one input region can given using the same format as the dependent input region.
将依赖区域相结合的分析表明独立的区域。只有一个输入区域可以使用相同的格式依赖输入区域。
参数：method
this must be the either full, window, overlap or smooth but the data should generated by the same method in integrated.analysis.
这必须是要么全，窗口，重叠或顺利，但数据应产生同样的方法在integrated.analysis。
参数：adjust
This variable must be a vector with the same length as samples or FALSE. The vector will be transformed to a factor and the levels of this will be coloured according to their subtype. When subtype=FALSE, all the samples will be coloured black.
这个变量必须是与samples或FALSE的相同长度的向量。向量将转化为一个因素，并根据其亚型水平，这将是彩色的。当subtype=FALSE，所有样品为黑色。
参数：significance
The threshold for selecting significant P-values.
选择显著P值的阈值。
参数：z.threshold
Threshold to display a green or red bar in the color bar on top of the heatmap for independent features with mean z-scores above z.threshold (high positive association) or below -z.threshold (high negative association).
阈值显示在绿色或红色的彩条平均Z-分数以上z.threshold（正相关），或低于-z.threshold（负相关）的独立功能热图上的条形。
参数：colRamp
Palette of colors to be used in the heatmap.
调色板在热图中使用的颜色。
参数：add.colRamp
Palette of colors to be used in the added plot.
要使用的颜色调色板中添加图。
参数：show.names.indep
logical if set to TRUE, displays the names (indep.id and in dep.symb entered in the assemble.data) of the independent features with mean z-scores above or below the z.threshold in the heatmap.
logical如果设置为true，显示的名称（indep.id和in dep.symbassemble.data的输入）Z-分数高于或低于平均独立的功能<X >中的热图。
参数：show.names.dep
logical if set to TRUE, displays the names (dep.id and dep.sy mb entered in the assemble.data) of the significant dependent features in the heatmap.
logical如果设置为TRUE时，显示的名称（dep.id和dep.sy mbassemble.data的进入）significant依赖热图功能。
参数：adjust.method
Method used to adjust the P-values for multiple testing, see p.adjust. Default is "BY" recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values.
用于调整多个测试P值的方法，看到p.adjust“。默认是“加”拷贝数时，建议使用相关数据。关于调整P值的更多信息，请参阅SIM卡。
参数：scale
Vector specifying the color scale in the heatmap. If scale="auto", the maximum and minimum value of all z-scores will be calculated and set as the limits for all analyzed regions. Another option is to define a custom scale, e.g. scale = c(-5,5).
向量在热图指定颜色的规模。如果规模=“自动”，所有的Z-分数最高和最低值将被计算和分析区域的限制。另一种方法是定义一个自定义的规模，例如规模= C（-5,5）。
参数：add.scale
Vector specifying the color scale in the left plot near the heatmap. If scale="auto", the maximum and minimum value of all the values will be calculated and set as the limits for all analyzed regions. Another option is to define a custom scale, e.g. scale = c(-5,5).
向量在附近的热图左图指定颜色的规模。如果规模=“自动”，最高和最低值的所有值将被计算和分析区域的限制。另一种方法是定义一个自定义的规模，例如规模= C（-5,5）。
参数：add.plot
Summary plot of copy number data in left panel. Either "smooth","heatmap", or "none". The "smooth" plot smoothes the copy number log ratios per sample, see quantsmooth for more details. The "heatmap" method produces an aCGH heatmap where green indicates gain, and red loss. The scale of the aCGH heatmap is automatically set to the min and max of the aCGH measurements of the analyzed regions. Default is plot.method = "none", no additional plot will be drawn.
在左侧面板中的拷贝数数据汇总图。要么"smooth"，"heatmap"或"none"。 "smooth"图抚平每个样品的log拷贝数比率，看到quantsmooth更多细节。 "heatmap"方法产生aCGH的热图，其中绿色表示增益和红色的损失。的aCGH热图规模将自动设置到最小和aCGH测量分析区域最大的。默认是plot.method ="none"，没有额外的图将绘制。
参数：smooth.lambda
Numeric value, specifying the quantile smoothing parameter for plot.method="smooth". See quantsmooth and references for more information.
，指定plot.method="smooth"的位数平滑参数的数值。 quantsmooth和references更多信息。
参数：pdf
logical; indicate whether to generate a pdf of the plots in the current working directory or not.
logical;指示是否在当前工作目录或图生成PDF。
参数：run.name
This must be the same a given to integrated.analysis
这必须是相同的一个给定的integrated.analysis
参数：...
not used in this version
在此版本中不使用
Details
详情----------Details----------
The sim.plot.zscore.heatmap function can only run after the integrated.analysis is run with zscores = TRUE.
只能运行后运行的integrated.analysissim.plot.zscore.heatmapzscores = TRUE函数。
The results are returned as a single-page pdf containing an association heatmap of the regions listed in input.regions. For high-density arrays large files will be produced, both demanding more memory available from your computer to produce them as well as being heavier to open on screen. To avoid this, analyze chromosome arms as units instead of chromosomes, both here and in input.regions = "all arms".
返回的结果将作为一个单页的PDF包含在input.regions所列区域的协会热图。对于高密度阵列将产生大文件，同时要求更多的内存可以从您的计算机生产以及较重的屏幕上打开。为了避免这种情况，为单位，而不是染色体分析染色体武器，在这里和在input.regions = "all arms"。
The heatmap contains the z-scores generated by the function integrated.analysis with zscores=TRUE. The dependent features are plotted from bottom to top, the independent features from left to right. Positive associations are shown in green, negative associations in red (color scale on the right). At the left side of the heatmap a color bar represents the multiple testing corrected P-values of the probes in the dependent data (copy number), also with a color legend. Dependening on which plot.method is used, a summary of copy number changes is shown on the left. At the top of the heatmap is a color bar corresponding to the mean z-scores of the independent features (expression data) that are above or below the z.threshold. If show.names.indep is set to TRUE, labels will be drawn for the probes with mean z-scores greater than z.threshold or lower than -z.threshold at the bottom of the heatmap. If show.names.dep is set to TRUE, labels will be drawn for the significant dependent probes lower than significance to the right of the heatmap.
热图包含Z-分数由zscores=TRUE功能integrated.analysis产生。相关的功能绘制从底部到顶部，从左至右的独立的功能。正相关，显示绿色，负红色协会（右边的颜色刻度）。在左侧的热图的彩条代表多个测试，校正P值的相关数据（拷贝数）的探针，也用一种颜色的传说。 Dependeningplot.method使用，拷贝数变化的总结显示在左边。在热图的顶部是一个颜色栏，相应的平均独立的功能（表达数据），高于或低于z.thresholdZ-分数。如果show.names.indep设置为TRUE时，标签将平均z分数大于z.threshold或低于探针绘制-z.threshold热图的底部。如果show.names.dep设置为TRUE时，标签将绘制为显着低于significance热图右侧的依赖探针。
值----------Value----------
No values are returned. The results are stored in a subdirectory of run.name as pdf.
没有返回值。结果被存储在run.name为PDF的子目录。
作者（S）----------Author(s)----------
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes <a href="mailto:R.X.Menezes@lumc.nl">R.X.Menezes@lumc.nl</a>
参考文献----------References----------
Quantile smoothing of array CGH data. Bioinformatics, 21(7):1146-53.
A method for calling gains and losses in array CGH data. Biostatistics, 6 :45-58.
参见----------See Also----------
SIM, tabulate.pvals, tabulate.top.dep.features, tabulate.top.indep.features, sim.plot.overlapping.indep.dep.features
SIM卡，tabulate.pvals，tabulate.top.dep.features，tabulate.top.indep.features，sim.plot.overlapping.indep.dep.features
举例----------Examples----------
#first run example(assemble.data)[第一次运行的例子（assemble.data）]
#and example(integrated.analysis)[和例如（integrated.analysis）]
#plot the zscores in a heatmap[1热图绘制的zscores]
sim.plot.zscore.heatmap(input.regions = "8q", adjust.method = "BY", run.name = "chr8q", pdf = FALSE)
sim.plot.zscore.heatmap(input.regions = "8q",
method="full",
significance = 0.05,
z.threshold = 1,
colRamp = colorRampPalette(c("red", "black", "green"))(15),
show.names.indep=TRUE,
show.names.dep=TRUE,
adjust.method = "holm",
add.plot = "heatmap",
smooth.lambda = 2,
pdf = FALSE,
run.name = "chr8q")
sim.plot.zscore.heatmap(input.regions = "8q",
method="full",
significance = 0.05,
z.threshold = 1,
show.names.indep = TRUE,
show.names.dep = TRUE,
add.plot = "none",
smooth.lambda = 2,
scale = c(-2, 2),
pdf = FALSE,
run.name = "chr8q")
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。
|
发表于 2012-2-26 14:24:33
