similar to: Inversions in hierarchical clustering were they shouldn't be

Hi, I am a newbie to the world of R. I have a data converted in csv format. Few cells in some of the rows of the data are blank ( in the sense that there is no value available for the particular experiment). When i try to open the file in R. I get an warning message. The specific warning message i get is {Warning message: NAs introduced by coercion } My data more or less looks like this -

I have 4 dell 2970 server , three server harddisk is 146Gx6 ,one hard disk is 72Gx6: each server mount info is /dev/sda4 on /exp1 type xfs (rw) /dev/sdb1 on /exp2 type xfs (rw) /dev/sdc1 on /exp3 type xfs (rw) /dev/sdd1 on /exp4 type xfs (rw) /dev/sde1 on /exp5 type xfs (rw) /dev/sdf1 on /exp6 type xfs (rw) I create a gluster volume have 4 stripe gluster volume create test-volume3 stripe 4

I do have a bunch of genes ( nearly ~50000) from the whole genome, which read in genomic ranges A range(gene) can be seem as an observation has three columns chromosome, start and end, like that seqnames start end width strand gene1 chr1 1 5 5 + gene2 chr1 10 15 6 + gene3 chr1 12 17 6 + gene4 chr1 20 25 6 + gene5

hi, folks: i need to transpose the following data: gene tissue patient1 patient2 patient3..... --------------------------------------------- gene1 breast 10 100 1 gene2 breast 20 200 4 gene3 breast 30 50 5 gene4 breast 40 400 9 ................................ to the

Hey guys, can anyone help? i have a sample table: >table <- structure(c(4, 7, 0.2, 3, .1, 7, 222, 3, 10, 5, 11, 8, 8, 10, 7), .Dim = c(5L, 3L), .Dimnames = list(c("gene1", "gene2", "gene3", "gene4", "gene5"), c("codon1", "codon2", "codon3"))) >table codon1 codon2 codon3 gene1 4.0 7

Hey guys, if i do a correspondance analysis, e.g.: table <- structure(c(4, 7, 0.2, 3, .1, 7, 222, 3, 10, 5, 11, 8, 8, 10, 7), .Dim = c(5L, 3L), .Dimnames = list(c("gene1", "gene2", "gene3", "gene4", "gene5"), c("codon1", "codon2", "codon3"))) Library(ca) plot(ca(table)) is there a way that i can see

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

I do have a bunch of genes ( nearly ~50000) from the whole genome, which read in genomic ranges A range(gene) can be seem as an observation has three columns chromosome, start and end, like that seqnames start end width strand gene1 chr1 1 5 5 + gene2 chr1 10 15 6 + gene3 chr1 12 17 6 + gene4 chr1 20 25 6 + gene5

Hi Everyone, I am trying to find a way to do this in excel to tell me which genes are the most differentially expressed. Sorry, i couldn't find excel forum section in nabble. However, if it is in R it is fine. This is a microarray data, and it has been normalized. According to Dov Stekel in Microarray, i will need to calculate log ratio (control-treatment). Once you have the log ratio,

>Date: Thu, 6 Mar 2008 06:46:07 -0800 (PST) >From: Keizer_71 <christophe.lo@gmail.com> >Subject: [R] Statistical Questions: finding differentially expressed >genes >To: r-help@r-project.org >Message-ID: <15873163.post@talk.nabble.com> >Content-Type: text/plain; charset=us-ascii >Hi Everyone, >I am trying to find a way to do this in excel to tell me which

Hi list, I have a question about using *read.table()* to read in a txt file. Basically, it consists of 16346 rows, 6 columns (no header). The code I used is: exprSet <- read.table('process_all4_GSA2.txt', row.names = 1,header =FALSE) and I got an error message: > exprSet <- read.table('process_all4_GSA2.txt', row.names = 1,header =FALSE) Error in

Hello, I'm a graduate student in Genetics, who has just started working with R. I have been trying to do a k-means clustering of an expression data compilation, which has lots of NA values in it. As suggested in a couple of earlier posts, I tried using na.omit() and the MICE imputation algorithm to take care of the NA, but they dont seem to work that well. na.omit() deletes the entries,

Starting with data from a microarray experiment and I would like to analyse the influence of two factors (age, treatment) on gene expression. Looking through the r-help archives and the web I tried the following: I put my data in a dataframe similar to this one: > example.df <- as.data.frame(matrix(data=runif(32,100,1000), nrow=4, ncol=4)) > example.df <-

Dear Dr. Liaw Andy: I have a few more questions about your heatmap function. actually heatmap is what I am looking for. heatmap(x, Rowv, Colv, distfun = dist, hclustfun = hclust, add.expr, scale=c("row", "column", "none"), na.rm = TRUE, ...) my data is a XNEW, > dim(XNEW) [1] 554 335 554 genes, 335 samples. now I want to use 1-CORR as a distance

Hi, I've looked through the posts but couldn't find a solution to this. I'd be really grateful if someone could help, I'd like to convert a data file of mutual information that is formatted as a matrix:             TF1    TF2    TF3    TF200... Gene1    0.0    0.2    0.2 Gene2    1.4    0.0    2.8 Gene3    0.3    0.6    1.7 Gene6000.... To a list: Gene1    TF1    0.0 Gene1   

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, I hope for some help of you. I have a dataframe and I want to regroup it. examp4.csv <http://r.789695.n4.nabble.com/file/n4648927/examp4.csv> I need the arguments of "VAL" as table heads and the "TYPE " only in individual expression. The result should look like in the example pic. exp4.png <http://r.789695.n4.nabble.com/file/n4648927/exp4.png> I

Hi all, I have been struggling with this problem for a few days. I have a data table like this: gene rpkm1 diff1 rpkm2 diff2 gene1 23 50 13 120 gene2 111 220 827 1200 gene3 75 998 71 910 And I want to re-format it so that, for each gene, I have a 2x2 contingency table, such as: gene rpkm diff gene1 23 50 gene1 13 120 gene2 111 220 gene2 827

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

Hi, Try this: lines1<- readLines(textConnection("gene1 or1|1234 or3|56 or4|793 gene4 or2|347 gene5 or3|23 or7|123456789")) lines2<-readLines(textConnection(">or1|1234 ATCGGATTCAGG >or2|347 GAACCTATCGGGGGGGGAATTTATATATTTTA >or3|56 ATCGGAGATATAACCAATC >or3|23 AAAATTAACAAGAGAATAGACAAAAAAA >or4|793 ATCTCTCTCCTCTCTCTCTAAAAA >or7|123456789