boothopach(hopach)
boothopach()所属R语言包：hopach
functions to perform non-parametric bootstrap resampling of hopach clustering results
功能执行非参数的bootstrap重采样的hopach聚类结果
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function boothopach takes gene expression data and corresponding hopach gene clustering output and performs non-parametric bootstrap resampling. The medoid genes (cluster profiles) from the original hopach clustering result are fixed, and in each bootstrap resampled data set, each gene is assigned to the closest medoid. The proportion of bootstrap samples in which each gene appears in each cluster is an estimate of the gene's membership in each cluster. These membership probabilities can be viewed as a "fuzzy" clustering result. The function bootmedoids take medoids and a distance function, rather than a hopach object, as input.
功能boothopach基因表达数据和相应的hopach基因的聚类输出和执行非参数的bootstrap重采样。该的medoid基因从原来的hopach聚类结果（聚类配置文件）是固定的，并在每个引导重采样的数据集，每个基因被分配到最接近的medoid。引导样本的比例，其中每个基因出现在每个聚类是一个基因的估计在每个聚类成员。这些成员概率可以被看作是一个“模糊”的聚类结果。函数bootmedoids作为输入中心点和一个距离函数，而非hopach的对象。“
用法----------Usage----------
boothopach(data, hopachobj, B = 1000, I, hopachlabels = FALSE)
bootmedoids(data, medoids, d = "cosangle", B = 1000, I)
参数----------Arguments----------
参数：data
data matrix, data frame or exprSet of gene expression measurements. Each column corresponds to an array, and each row corresponds to a gene. All values must be numeric. Missing values are ignored.
数据矩阵，基因表达测量的数据框或exprSet。每列对应一个数组，每一行对应一个基因。所有的值必须是数字。遗漏值将被忽略。
参数：hopachobj
output of the hopach function.
hopach函数的输出。
参数：B
number of bootstrap resampled data sets.
举重采样的数据集的数目。
参数：I
number of bootstrap resampled data sets (deprecated, retaining til v1.2 for back compatibility).
引导重采样的数据集（废弃，直到V1.2保留回兼容性）。
参数：hopachlabels
indicator of whether to use the hopach cluster labels hopachobj$clustering$labels for the row names (TRUE) versus the numbers 0 to 'k-1', where 'k' is the number of clusters (FALSE).
指标是否使用的hopach聚类标签hopachobj$clustering$labels行名称（TRUE），与“K-1的数字0，K是数字聚类（假）。
参数：medoids
row indices of data for the cluster medoids.
data聚类中心点行指数。
参数：d
character string specifying the metric to be used for calculating dissimilarities between vectors. The currently available options are "cosangle" (cosine angle or uncentered correlation distance), "abscosangle" (absolute cosine angle or absolute uncentered correlation distance), "euclid" (Euclidean distance), "abseuclid" (absolute Euclidean distance), "cor" (correlation distance), and "abscor" (absolute correlation distance). Advanced users can write their own distance functions and add these.
字符串指定的度量用于计算向量之间的异同。目前可用的选项是“cosangle”（余弦角或者非中心的相关距离），的“abscosangle”（绝对余弦角度或绝对非中心相关距离），“欧几里得”（欧氏距离），的“abseuclid”（绝对欧氏距离），“心病”（相关距离），和“abscor”（绝对相关距离）。高级用户可以编写自己的距离函数，并添加这些。
Details
详情----------Details----------
The function boothopach requires only data and the corresponding output from the HOPACH clustering algorithm produced by the hopach function. The function bootmedoids is designed to work for any clustering result; the user imputs data, medoid row indices, and the distance metric. The supplied distance metrics are the same as for the distancematrix function. Each non-parametric bootstrap resampled data set consists of resampling the 'n' columns of data with replacement 'n' times. The distance between each element and each of the medoid elements is computed using d for each bootstrap data set, and every element is assigned (for that resampled data set) to the cluster whose medoid is closest. These bootstrap cluster assignments are tabulated over all I bootstrap data sets.
功能boothopach只需要数据和从hopach函数HOPACH聚类算法产生相应的输出。函数bootmedoids任何聚类结果的设计工作;用户imputs数据，medoid行指数，距离度量。提供距离度量作为distancematrix函数的相同。每个非参数的bootstrap重采样数据集，包括重采样的ndata替代N次列。每个元素的的medoid元素的每个之间的距离计算使用d每个引导数据集，每个元素分配（该重采样的数据集）medoid最接近的聚类。所有I引导数据集，这些引导聚类分配表。
值----------Value----------
A matrix of bootstrap estimated cluster membership probabilities, which sum to 1 (over the clusters) for each element being clustered. This matrix has one row for each element being clustered and one column for each of the original clusters (one cluster for each medoid). The value in row 'j' and column 'i' is the proportion of the I bootstrap resampled data sets that element 'j' appeared in cluster 'i' (i.e. was closest to medoid 'i').
引导估计聚类成员的概率，总结1（在聚类）被聚类的每个元素的矩阵。这个矩阵被聚类的每个元素有一行和一列原来的聚类（聚类）每个medoid。行“J”和列值我是我举的比例重采样数据集，元素J聚类出现的“i”（即最接近medoid我）。
作者（S）----------Author(s)----------
Katherine S. Pollard <kpollard@gladstone.ucsf.edu> and Mark J. van der Laan <laan@stat.berkeley.edu>
参考文献----------References----------
参见----------See Also----------
distancematrix, hopach
distancematrix，hopach
举例----------Examples----------
#25 variables from two groups with 3 observations per variable[25两组每3个变量观测变量]
mydata<-rbind(cbind(rnorm(10,0,0.5),rnorm(10,0,0.5),rnorm(10,0,0.5)),cbind(rnorm(15,5,0.5),rnorm(15,5,0.5),rnorm(15,5,0.5)))
dimnames(mydata)<-list(paste("Var",1:25,sep=""),paste("Exp",1:3,sep=""))
mydist<-distancematrix(mydata,d="cosangle") #compute the distance matrix.[计算距离矩阵。]
#clusters and final tree[聚类和最终树]
clustresult<-hopach(mydata,dmat=mydist)
#bootstrap resampling[举重采样]
myobj<-boothopach(mydata,clustresult)
table(apply(myobj,1,sum)) # all 1[所有1]
myobj[clustresult$clust$medoids,] # identity matrix[单位矩阵]
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。
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发表于 2012-2-25 21:44:54
