similar to: reshaping list into a contingency table

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, 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

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

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

Hi, this might be basic but can't get it to work and it is hampering my R usage: #the loop is checking variance of rows, and cutting out rows with var>numVec[i] #I define outMat as object names I want to output to (does this make sense? how else #can I define sequential numbered output?) #numVec is numbers I use in the loop head(Counts) AN1 AN2 AN3 AN4 var GENE1

hi, everyone: i have a question on the reshape function. i have the following dataset : gene tissue patient1 patient2 patient3............. _________________________________________________ gene1 breast 10 20 50 gene2 breast 20 40 60 gene3 breast 100 200 300 which i hope to convert to the following format: gene patientID

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

Hello all! I have the following problem with the %in% command: 1) I have a data frame that consists of functions (rows) and genes (columns). The whole has been loaded with the "read.delim" command because of gene-duplications between the different rows. 2) Now, there is another data frame that contains all the genes (only the genes and without duplicates) from all the functions of

Dear all, Once again I need your help ; I fond a way to do what I want but I am sure there is a better way.. maybe you can help me. I have a matrix, for example mat.tot : > mat.tot ID Desc M1 M2 1 1 gene1 0.5 0.2 2 2 gene2 -0.4 -0.1 3 3 gene3 1.0 1.2 4 4 gene1 0.6 0.3 5 5 gene2 -0.3 0.0 and I want to merge line 1 with line 4, and line 2 with line 5 because this is the same gene. I can

Hey guys, Can anyone help? I did a correspondance analysis and made a plot. I also have a specific list of nodes that i want to find in my plot and want to either color the nodes that appear in my list differently, or put some kind of border around that group of nodes... Would anyone know how to do this? Also, would this post be more relevant here or in the bioconductor forum? -- View this

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)

I have 2 files containing data analysed by 2 different methods. I would like to find out which genes appear in both analyses. Can someone show me how to do this? _________________________________________________________________ [[trailing spam removed]] [[alternative HTML version deleted]]

hi I want to compare two list by its names and get the values of that list. can anybody let me know the syntax of comparing the list by their names using a for loop c.genes<- list() for(i in 1:100) c.genes[[1]]<- geneset(which(geneset == tobecampared[i])) } here geneset is a list and also tobecampared is a list Thank you Ramya -- View this message in context:

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

Hi, I'm sure this is pretty straightforward, but I cant seem to figure it out after playing around with grid, and a number of other packages for graphing I have data of the following type (much larger of course, and in this case is named "Medoid4"). The first column defines the x-axis, while as each other column is individual y-axes in different plots gene1 gene2 gene3 4

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,

Hello: I am a novice R user, but I have been working my way through the manuals / tutorials, ... I have R / Deducer up and running, and know the basics. I want to analyze a microarray (gene expression) dataset. I need to input the data into R as a multidimensional (multi-way) array, something on the order of 15,000 x 3 x 8 x 2 [genes x replicates x time points x treatments] I've