共识树或“引导比例”来自多个 hclust 对象
我有一个由一个变量的微小变化（用于计算距离矩阵）产生的 hclust 对象列表，
- 现在我想从这个列表中创建一个共识树。
有通用包可以做到这一点吗？我正在破解我的出路 maanova 的一些代码似乎可以工作 - 但它很丑陋而且它 需要大量的黑客工作，因为我没有进行“正常”的引导（它是 化学数据）。
/Palle Villesen，丹麦
c1_list <- seq(10,100,by=10)
c2 <- 30
e<- 1
mboot <- list()
for (i in 1: length(c1_list) ) {
c1 <- c1_list[i]
cat("Doing C1=",c1,"...")
x <- hclust(custom_euclidean(t(log2(data[, all]+1)), c1,c2,e), method='average')
cat("..done\n")
mboot[[i]] <- x # To get hclust object back use mbot[[i]] to get i'th object
}
#### Now extract the robust groups from mboot...
I have a list of hclust objects resulting from slight variations in one variable (for calculating the distance matrix)
- now I would like to make a consensus tree from this list.
Is there a generic package to do this? I am hacking my way through
some code from maanova and it seems to work - but it's ugly and it
needs a lot of hacking since I am not doing "normal" bootstrapping (it's
chemical data).
/Palle Villesen, Denmark
c1_list <- seq(10,100,by=10)
c2 <- 30
e<- 1
mboot <- list()
for (i in 1: length(c1_list) ) {
c1 <- c1_list[i]
cat("Doing C1=",c1,"...")
x <- hclust(custom_euclidean(t(log2(data[, all]+1)), c1,c2,e), method='average')
cat("..done\n")
mboot[[i]] <- x # To get hclust object back use mbot[[i]] to get i'th object
}
#### Now extract the robust groups from mboot...
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首先，看一下 Allan Tucker 的共识集群代码，与他的相关论文“基因表达数据的共识聚类和功能解释”。
以下是其他一些提示：
First, have a look at Allan Tucker's code for consensus clustering, related to his paper "Consensus Clustering and Functional Interpretation of Gene Expression Data".
Here are a few other pointers:
consensus()function. Have you tried that?嗯，这听起来像是应用于集群的增强方法，快速的 Google 搜索揭示了关于 增强集群。也许这是一个开始？
至于R代码，总是有关于聚类的任务视图和机器学习。
Hm, that sounds like a boosting approach applied to clustering, and a quick Google search reveals quite an existing literature on boosting clustering. Maybe that is a start?
As for R code, there are always the Task Views on Clustering and Machine Learning.