similar to: Statistical Questions: finding differentially expressed genes

>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

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, 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

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, 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,

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

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

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)

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

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 <-

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,when I perform SAM on my array data(siggenes)I have some problems in retrieving the separate lists of up regulated and down regulated genes. When I write: fold<-function(x){ gruppi<-split(x,controllo) geni1<-abs(mean(gruppi[[2]])-mean(gruppi[[1]])) return(geni1) } fold<-esApply(expr.contr.tratt.4,1,fold)

Hi I have been asked by a colleague to perform a statistical analysis which uses random effects - but I am struggling to get this to work with nlme in R. Help would be very much appreciated! Essentially, the data consists of: 10 patients. Each patient has been given three different treatments (on three separate days). 15 measurements (continuous variable) have been taken from each patient

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   

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

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

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

> dim(data.sub) [1] 10000 140 #####extracting all differentially express genes########## library(multtest) two_side<- (1-pt(abs(data.sub),50))*2 diff<- mt.rawp2adjp(two_side) all_differ<-diff[[1]][37211:10000,] all_differ #####list of differentially expressed genes########## > probe.names<- + all_differ[[2]][all_differ[[1]][,"BY"]<=0.01] Error in

Hi, May be you can try this: dat1New<-? dat1[!(duplicated(dat1$gene)|duplicated(dat1$gene,fromLast=TRUE)),] dat2<-dat1[duplicated(dat1$gene)|duplicated(dat1$gene,fromLast=TRUE),] ?lst1<-split(dat2,dat2$gene) dat3<-unsplit(lapply(lst1,function(x) {x1<- sum(apply(x[,6:32],2,function(y) y[1]>=y[2]));x2<- sum(apply(x[,6:32],2, function(y) y[1]<=y[2])); if(x1>x2) x[1,] else