similar to: Re: Clustering in R

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

Hi, I have a gene expression experiment with 20 samples and 25000 genes each. I'd like to perform clustering on these. It turned out to become much faster when I transform the underlying matrix with t(matrix). Unfortunately then I'm not anymore able to use cutree to access individual clusters. In general I do something like this: hc <- hclust(dist(USArrests), "ave")

I'm clustering rather large data sets and would like to cut the dendrograms to get a better view of specific components. I calculate the dissimilarity matrix using daisy() because I have a mixture of variable types: factors, ordered factors and numerical variables. If I want one dendrogram, I use agnes() for the agglomerative nesting and pltree() to draw the dendrogram. That way, I get the

Good afternoon, I ran heatmap and hclust on the same matrix x (strictly, I ran heatmap(x), and hclust(dist(t(x))), and realized that the two dendrograms were slightly different, in that the left-right arrangement of one pair of subclusters (columns) was reversed in the two functions (but all individual columns were grouped correctly). Looking through the code for heatmap as a most definite

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

I'd like to be able to cut dendrograms at a height I specify and then plot the resulting subtrees. I wanted to use the dendrogram object for this purpose because there doesn't seem to be a canned way to cut a hclust object and get a list of hclust objects, but there is a function (cut) that does that for dendrograms. The problem I'm having is that when I plot a dendrogram, I

I would like to draw horizontal or vertical lines on a heatmap to highlight the clusters at some specified cut depth of the dendrogram. As a hacked example, the following code would work if I could set the coordinates of the top and bottom of the false color image correctly (ymin and ymax), but the correct values seem to depend on the output device and its size. I realize that heatmaps use a 2x2

Hello all, A question/suggestion: I was wondering if there is a chance of changing stats::cutree to be S3 and use cutree.hclust? For example: cutree <- function(tree, k = NULL, h = NULL,...) { UseMethod("cutree") } cutree.hclust <- stats::cutree # This will obviously need the actual content of stats::cutree This would be nicer for people like me to add new methods to

I generated a heatmap in R using the following commands: > mydata <- read.csv(file="Data.csv", header=TRUE, sep=",") > mydata <- mydata[rowSums(mydata[,-1]^2) >0, ] > rownames(mydata)=mydata$Name > mydata <- mydata[,2:253] > mydatamatrix <- data.matrix(mydata) > mydatascale <- t(scale(t(mydatamatrix))) > hr <-

I generated a heatmap in R using the following commands: > mydata <- read.csv(file="Data.csv", header=TRUE, sep=",") > mydata <- mydata[rowSums(mydata[,-1]^2) >0, ] > rownames(mydata)=mydata$Name > mydata <- mydata[,2:253] > mydatamatrix <- data.matrix(mydata) > mydatascale <- t(scale(t(mydatamatrix))) > hr <-

Well, you don't give much of an example.... I'm replying CC to the R mailing list. Please ask questions there, rather than adressing individuals for basic help. Here is one; does it answer your question ? data(agriculture) ag.ag <- agnes(agriculture) class(ag.ag) pltree(ag.ag) # the dendrogram, if you want to see it ## cut the dendrogram -> get cluster assignments: (ck3 <-

I have used heatmap to visualize my microarray data. I have a matrix of M-values. I do the following. #The distance between the columns. sampdist <- dist(t(matrix[,]), method="euclidean") sclus <- hclust(sampdist, method="average") #The distance between the rows. genedist <- dist(matrix[,], method="euclidean") gclus <- hclust(genedist,

Hi Sorry if this is in the help :-S I've looked at example(dendrogram) and though it gives some indication of what I want, it doesn't do all. OK, so here is what I want to do: draw a tree, and then have an action, on user-click, to either draw a sub tree or a plot of the data. I also want users to be able to search for a particular label and have it highlighted on the tree, say in

I've found that while cutree() and rect.hclust() make the same classes for a given height in the dendrogram, the actual labeling of the classes is different. For example, both produce the same 4 classes but class 1 according to cutree() is class 4 according to rect.hclust(). Would it be possible that future versions provide the same labeling? rect.hclust() is useful to display the classes

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by

Hi, I have the distance matrix computed and I feed it to hclust function. The plot function produces a dense dendrogram as well. But, the cutree function applied does not produce the desired list. Here is the code x=data.frame(similarity_matrix) colnames(x) = c(source_tags_vec) rownames(x) = c(source_tags_vec) clust_tree=hclust(as.dist(x),method="complete") plot(clust_tree)

Hello R-Helpers, I have a question about extracting the clusters of genes after we make the heatmap (say ht4) using the heatmap.2 function. Basically, I want to get the clusters which are shown as row dendrogram in the heatmap. I understand that ht4$rowDendrogram is an object of dendrogram and it containes details of all the nodes and branches, but lets say I want to know the number of clusters

> I have performed clustering analysis with hclust (Ward's method) on a > database of 800 samples. As you may imagine the full dendrogram is not > really readable. I have obtained groups with cutree. I would like to plot > sub-sections of my big dendrogram to show group 1, group 2 and so on. I don't think R has anything like subtree in Splus, unfortunately. I think what has