similar to: help with plot hclust tree

Has anyone successfully created a PNG file for a dendrogram? I am able to successfully launch and view a dendrogram in Quartz. However, the dendrogram is quite large (too large to read on a computer screen), so I am trying to save it to a file (1000x4000 pixels) for viewing in other apps. However, whenever I try to initiate a PNG device, I get a "need finitite 'xlim' values"

Hello, I have been using the "cophenetic" function for objects of class "dendrogram" and I have realised that it gives different results when it is used with objects of class "hclust". For instance, running the first example in the help file of the "cophenetic" function, d1 <- dist(USArrests) hc <- hclust(d1, "ave") d2 <-

Note that cophenetic.default (which works on the output of hclust(dist(X))) uses the row names of X as labels. as.dendrogram.hclust does not retain those row names so cophenetic.dendrogram cannot use them (so it orders them based on the topology of the dendrogram). Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, Apr 21, 2016 at 7:59 AM, William Dunlap <wdunlap at tibco.com> wrote:

I am making heatmaps for a dataset (~ 300*600 matrix) with the following R script (I am not familiar with R and this is the first time I am using it). library("gplots") library("Cairo") mydata <- read.csv(file="data.csv", header=TRUE, sep=",") rownames(mydata)=mydata$Name mydata <- mydata[,2:297] mydatamatrix <- data.matrix(mydata) mydatascale

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'm trying to save heatmaps as PDFs. However, the PDF version of the heatmaps (Heatmap_CAFvsTNF_run2.pdf) is blurred when compared to its counterpart, which was saved manually by using the software "Grab" (Heatmap_CAFvsTNF_run2.tiff). -----R code-------- sample_output <- "stroma_run2" filename <-

Hi, I'm doing hierarchical clustering, and want to export my dendrogram to a tree-viewing/editing software. I can do this by converting the data to Newick format (hc2Newick in ctc package), but I can't get branch lengths to show in the resulting phylogram. I figured it might help to convert my hclust object into a phylo object (as.phylo in ape package), but the following lines give me

Dear All According to the identify.hclust documentation the function "cuts the tree at the vertical position of the pointer and highlights the cluster containing the horizontal position of the pointer". When I carry out this, the tree isn't cut where I click - in fact, there seems to be a limit below which I cannot go. Consider the following code: mat <- matrix(rnorm(5000),

Hello all, I posted a question to this list last week and received no response. I am unsure if this means no-one knows the answer or if I posed the question badly. I'm going to assume I posed the question badly and try again. I am new to R so it is quite likely it's a very naive question, however if there is something blindingly obvious that I am missing or if there is another resource I

Hi All R-helpers This is my first (but probartly not last ;-) mail to R-help, so hello to everybody. My problem: Is there a way to give colors to the labels (sample labels) in plots for a hclust object for better visualization? I have looked through plot, points, hclust and more but cannot find anything on label color. Anybody know if this is doable? Best regards Jeppe

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

Dear All, I wrote a function for cluster analysis to compute cophenetic correlations between dissimilarity matrices (using the VEGAN library) and cluster analyses of every possible clustering algorithm (SEE ATTACHED) http://old.nabble.com/file/p26288610/cor.coef.R cor.coef.R . As it is now, it is extremely long, and for the future I was hoping to find a more efficient way of doing this sort of

Dear R Helpers, I read carefully the documentation and all postings on the hclust and cutree functions, however some aspects of the tree ordering and cluster assignment performed by these functions remain unclear to me, so I would very much appreciate your help in making sure I get them right. Here is an example, with values chosen to illustrate the problems. I have a set of five profiles

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've been trying to get plot.hclust to work with a hclust object I created and have not had much success. It seems that there is some "hidden" characteristic of a hclust object that I can't see. This is most easily seen in the following example, where plot.hclust works on one object, but when this object is "dumped" and then re-read, plot.hclust no longer works. Is

Hi R helpers... I am having troubles because of the discrepancy between the dendrogram plotted from hclust and what is wrote in the hc2Newick file. I've got a matrix C: > hc <- hclust(dist(C)) > plot(hc) with the: > write(hc2Newick(hc),file='test.newick') both things draw completely different "trees"... I have also tried with the raw distance matrix D and

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 <-

Having some problems with using hclust.identify on my data, I revert to trying it out on the example in the help manual. My result is still the same (as with my own data): data(iris) hci <- hclust(dist(iris[,1:4])) plot(hci) testx<-identify.hclust(hci) Error in rect.hclust(x, k = k, x = x$x, cluster = cluster[, k - 1], border = "red") : k must be between 2 and 0

Hello all, I'm now working with hclust objects and was hoping to perform some basic editing on them like: - Joining = the merging of two hclust objects (so they will share one root) - Splicing = So to cut/extract a branch out of an hclust object - that by itself will be an hclust object. I noticed I could extract one element of an hclust object by turning it into a dendrogram,

Hello, I'm having trouble figuring out how to see resulting groups (clusters) from my hclust() output. I have a very large matrix of 4371 plots and 29 species, so simply looking at the graph is impossible. There must be a way to 'print' the results to a table that shows which plots were in what group, correct? I've attached the matrix I'm working with (the whole thing