similar to: re: two dimentional hierarchical clustering algorithm

Hi, I''m using heatmap.2 to cluster my data, using the centroid method for clustering and the maximum method for calculating the distance matrix: library("gplots") library("RColorBrewer") test <- matrix(c(0.96, 0.07, 0.97, 0.98, 0.50, 0.28, 0.29, 0.77, 0.08, 0.96, 0.51, 0.51, 0.14, 0.19, 0.41, 0.51), ncol=4, byrow=TRUE)

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

heatmap.2 defaults to dist for calculating the distance matrix and hclust for clustering. Does anyone now how I can set dist to use the euclidean method and hclust to use the centroid method? I provided a compilable sample code bellow. I tried: distfun = dist(method = "euclidean"), but that doesn't work. Any ideas? library("gplots") library("RColorBrewer") test

Hi netters, I have a matrix X of the size (1000,100). The values are from -3 to +3. When I tried heatmap(X, distfun=function(c),dist(c,method="bin"),hclustfun=function(m),hclust(m,method="average")) I got the error message: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? However, if I used default parameters for distfunction:

Dear Experts, I am using the below script to generate the heat map of gene expression data. I am using Hierarchical Clustering (hclust) for clustering. Now I want to compare different clustering parameters such as *K-means* clustering, Model Based Clustering, I have two queries: 1. How to incorporate different clustering method in the same code? 2. Is this possible to implement pvclust in the

Hi, I run into following error when using heatmap() for data matrix "xx". Any help is appreciated? "xx" contains many "NA"s. > hv <- heatmap(data.matrix(xx)) Error in hclustfun(distfun(if (symm) x else t(x))) : NA/NaN/Inf in foreign function call (arg 11) Thanks a lot. Yuhong

Hi all, I am a novice user of the heatmap.2 function in the Bioconductor gplots package. I am trying to create a heatmap diagram from a hierarchical clustering result based on the complete linkage and the pearson correlations as the distance metrics. After reading the help page for heatmap.2, it seems that it calls the distfun and hclustfun with the default settings. Is that the same as doing

Hi, there, I am planning to use heatmap.2 function to draw some heatmaps. However, I don't want to use default set manhattan distance and complete linkage in heatmap.2. How should I change parameters distfun and hclustfun? Thank you. ys [[alternative HTML version deleted]]

Hi, Could you please help? Heatmap doesn't work with: > heatmap(as.matrix(SPIV2),na.rm = T) Error in hclustfun(distfun(x)) : NA/NaN/Inf in foreign function call (arg 11) There are no 0 data rows or column Thanks a lot Regards --------------------------------------- David

Hi, I am using heatmap.2 of gplots to make heatmaps of my the attached file. I am giving my code for the same .. library(gplots) x=read.table("1.txt", header=TRUE) mat=data.matrix(x) heatmap.2(mat, col=greenred(75), Rowv=TRUE, Colv=TRUE, distfun = dist, hclustfun = hclust, dendrogram = c("both"), scale = c("row"), na.rm=TRUE, trace="none",

Hello, I am trying to create a heatmap that clusters based on a k-means scheme rather than a hierarchical clustering scheme. Suppose I have the following input data, located in sample.table: x1 x2 x3 x4 x1 17.198 16.306 16.806 16.374 x2 14.554 10.866 15.780 14.596 x3 14.374 14.118 14.569 17.352 x4 17.505 14.596 15.738 14.070 By using the heatmap.2 function as follows, I can create a heatmap

Hi, I am drawing a heat map using gplots of R package. The file (new4) is having values 0 and 1 only. library(gplots) library(marray) x=read.table("new4", header=TRUE) mat=data.matrix(x) heatmap.2(mat, col=colorRampPalette(c("black","red"))(256), #col=greenred(75), Rowv=TRUE, Colv=TRUE, distfun = dist, hclustfun = hclust, dendrogram = c("row"), scale =

Hi, Can anyone tell me how to set Z-score according to my own requirement as the below code is taking as per the file entries. Any help would be appreciable. library(gplots) x=read.table("final.txt", header=TRUE) mat=data.matrix(x) heatmap.2(mat, col=colorRampPalette(c("green","white","red"))(256), #col=greenred(75), Rowv=TRUE, Colv=FALSE, distfun = dist,

Hi All Not sure if this a bioconductor question or general R mailing list so apologies if this has gone to the wrong one................. When plotting dendrograms created by hclust you can "identify" clusters by clicking on the graphics and returning a list of what is contained in each cluster. However I'd like to be able to "zoom in" on specific clusters and plot

I have written a wrapper for heatmap.2 called heatmap.w.row.and.col.clust which auto-generates breaks using breaks<-round((c(seq(from=(-20 * stddev), to=(20 * stddev))))/20, digits = 2) #(stddev in this case = 2.5) This has always worked well in the past but now I am getting an error that non-finite breaks are being generated. Drilling down, it seems that my wrapper is generating finite

Hi,   I need to generate a heatmap on a square matrix and wouldn't want to reorder the columns and the rows on the heatmap display.    I have used the options Rowv=NULL and Colv=NULL but doesn't seem to work. Following is a snippet of the heatmap function i am using. args <- commandArgs(); inputfile <- args[2] imgfile   <- args[3] bitmap(imgfile, height=15, width=15, res=100,

Dear Researchers, I wrote two function to fit a circle using noisy data. 1- the fitCircle() is derived from MATLAB code of * zhak Bucher* from the link http://www.mathworks.com/matlabcentral/fileexchange/5557-circle-fit/content/circfit.m 2- the CircleFitByPratt() from MATLAB code of *Nikolai Chernov *from the link

Hi all (especially R-core) I suppose, With the introduction of the new functional programming functions into base I thought I'd ask for a Curry() function. I use a simple one that looks this: Curry = function(FUN,...) { .orig = list(...);function(...) do.call(FUN,c(.orig,list(...))) } This comes in really handy when using say, heatmap():

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Dear Experts, The following is my "Iterator". When I try to write a new function with itel, I got error. This is what I have: > supDist<-function(x,y) return(max(abs(x-y))) > > myIterator <- function(xinit,f,data=NULL,eps=1e-6,itmax=5,verbose=FALSE) { + xold<-xinit + itel<-0 + repeat { + xnew<-f(xold,data) + if (verbose) { + cat( +