heatmapPhenoTest(phenoTest)
heatmapPhenoTest()所属R语言包：phenoTest
Produce heatmap from phenotype data.
生产型数据的热图。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Show the associations between clusters that each sample belongs to and each phenotype in a heatmap and/or a Kaplan-Meier plot.
每个样品属于聚类，每一个热图和/或采用Kaplan-Meier曲线型之间的关联。
用法----------Usage----------
heatmapPhenoTest(x, signatures, vars2test, probes2genes = FALSE,
filterVar, filteralpha = 0.05, distCol = "pearson", nClust = 2, distRow
= "cor", p.adjust.method = "none", simulate.p.value = FALSE, B = 10^5,
linkage = "average", equalize = FALSE, center = TRUE, col, survCol,
heat.kaplan="both", ...)
参数----------Arguments----------
参数：x
ExpressionSet with phenotype information stored in pData(x).
ExpressionSet储存在pData(x)型的信息。
参数：signatures
Either character vector or list of character vectors with gene sets to be used to draw heatmaps (gene names should match those in featureNames(x)). A separate heatmap will be produced for each element in the list.
要么与基因特征向量的特征向量或列表将被用来绘制热图（基因名称应该符合那些在featureNames(x)）。列表中的每个元素将产生一个单独的热图。
参数：vars2test
list with components 'continuous', 'categorical', 'ordinal' and 'survival' indicating which phenotype variables should be tested. 'continuous', 'categorical' and 'ordinal' must be character vectors, 'survival' a matrix with columns named 'time' and 'event'. The names must match names in names(pData(x)).
组件的持续“，”分类“，”序“和”生存“型变量应测试的名单。 “连续”，“类别”和“序”必须是特征向量，“生存”，一个名为“时间”和“事件”的列矩阵。该名称必须匹配名names(pData(x))。
参数：probes2genes
If set to TRUE a single probe is selected for each gene. nsFilter is used to select the probe with highest inter-quartile range.
如果设置为TRUE一个探针，每一个基因的选择。 nsFilter用来选择与探针间最高四分范围。
参数：filterVar
If specified, only genes with significant differences in the variable filterVar will be displayed in the heatmap. Note that this option will not affec the sample clustering, as this is obtained using both significant and non-significant genes.
如果指定，唯一的基因显着性差异的变量filterVar将显示在热图。请注意，此选项不会affec样本聚类，因为这是利用重大和非重大的基因。
参数：filteralpha
Significance level for the filtering based on filterVar.
筛选的基础上filterVar显着性水平。
参数：distCol
Distance metric used to cluster columns (e.g. patients/samples). Can take any value accepted by dist. Pearson and Spearman correlations are also allowed. Write 'spearman' or 'pearson' to use them.
距离度量用于聚类的列（如病人/样品）。可以采取dist接受任何价值。也允许Pearson和Spearman相关。写斯皮尔曼或皮尔森使用它们。
参数：nClust
Number of desired clusters.
所需的聚类数目。
参数：distRow
Distance metric used to cluster rows (e.g. genes). Can take any value accepted by distancematrix.
用于聚类行（如基因）的距离度量。可以采取distancematrix接受任何价值。
参数：p.adjust.method
Method for P-value adjustment, passed on to p.adjust.
P值调整方法，通过p.adjust。
参数：simulate.p.value
If set to FALSE the chi-square test p-values are computed using asymptotics, otherwise a simulation is used (see chisq.test for details).
如果设置为FALSE使用渐近P-值计算的卡方检验，否则仿真（见chisq.test细节）。
参数：B
An integer specifying the number of replicates used in the chi-square Monte Carlo test (passed on to chisq.test).
一个整数，指定复制的数量，使用卡方的蒙特卡洛试验（chisq.test的传递）。
参数：linkage
Linkage used for clustering. Must be either 'complete', 'average' or 'minimum'.
为聚类联动使用。必须是“完整”，“平均”或“最低”。
参数：equalize
Should color codes be equalized between genes, i.e. all genes present the same range of colors. Passed on to heatmap_plus.
应颜色编码基因之间的均衡，颜色相同的范围，即所有基因。传递heatmap_plus。
参数：center
centering is done by subtracting the column means (omitting NAs).
定心做减去柱手段（忽略NAS）。
参数：col
Color scheme to be used for heatmap. Defaults to a green/red scheme designed to look nice for microarray data.
配色方案，可用于热图。默认为绿色/红色方案设计看起来不错的微阵列数据。
参数：survCol
Colors for the Kaplan-Meier survival curves.
Kaplan-Meier生存曲线的颜色。
参数：heat.kaplan
can be "heat" if we want to plot a heatmap, "kaplan" if we want to plot a kaplan-meier or "both" if we want both of them.
可以是“热”，如果我们要绘制1热图，“卡普兰”如果我们要绘制Kaplan-Meier法或“both”如果我们希望他们两个人。
参数：...
Other arguments for the survival plot, e.g. lty etc.
其他参数的生存图，例如LTY等。
Details
详情----------Details----------
Makes two clusters of samples based on the expression levels of the genes from the given signature and plots a heatmap and/or a Kaplan-Meier showing the association between belonging to one cluster or the other and each phenotype.
使两个样品从给定的签名和绘制热图和/或采用Kaplan-Meier显示属于一个聚类或其他各型之间的关联的基因表达水平的聚类。
For variables in vars2test\$continuous and vars2test\$ordinal a Kruskal-Wallis Rank Sum test is used; for vars2test\$categorical a chi-square test (with exact p-value if simulate.p.value is set to TRUE); for var2test\$survival a Cox proportional hazards likelihood-ratio test.
在vars2test变量\ $连续vars2test \ $序1克鲁斯卡尔-Wallis秩和检验; vars2test \ $类别卡方检验（精确p值如果simulate.p.value设置为TRUE）; var2test \ $生存Cox比例风险似然比检验。
作者（S）----------Author(s)----------
David Rossell
举例----------Examples----------
#load data[数据加载]
data(eset)
eset
#construct vars2test[构建vars2test]
survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE)
colnames(survival) <- c('event','time')
vars2test <- list(survival=survival)
vars2test
#construct a signature[建立一个签名]
sign <- sample(featureNames(eset))[1:20]
#make plot[使图]
heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='heat')
heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='kaplan')
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 11:05:37
