search for: gene1

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    TF2    0.2    Gene1    TF3    0.2 Gene2    TF1    1.4 Gene2    TF2    0.0 Gene2    TF3    2.8 Gene3    TF1    0.3 Gene3    TF2    0.6 Gene3    TF3    1.7 Gene6000...TF2...

...trying to fetch rows from a data frame which matches to first 2 columns of another data frame. Here is the example what I am trying to do: > ptable=read.table(file="All.txt",header=T,sep="\t") > ptable=as.matrix(ptable) > dim(ptable) [1] 9275 6 > head(ptable) Gene1 Gene2 PCC PCC3 PCC23 PCC123 [1,] "3813_f_at" "3884_f_at" "0.9956842" "0.9955455" "0.9956513" "0.9956171" [2,] "3884_f_at" "3813_f_at" "0.9956842" "0.9955455" "0....

Hello everyone, I have a data frame (tt), see below (I only show 2 genes, actually I have a lot): group gene1 gene2 Control 28.9776 9.9355 Control 28.9499 10.0997 Control 29.5468 14.2995 Control 29.5246 13.9561 Test1 29.1864 9.7718 Test1 29.2048 10.0388 Test1 34.9563 11.9509 Test1 34.9464 11.8909 Test2 36.9566 14.5316 Test2 37.1309 14.5188 Test2 36.1017 29.5468 Test2 36.0883 29.5246 I'd...

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 value gene1 ----------------------------- gene1 1 10 10 gene1 2 20 20 gene1 3...

...s tissue 3, 4,5,6. The other labs has one tissue type each. The 'sample' data is as below: ------------------------------------------------------------------------------------------------ Sample.ID Gene tissue.type batch(lab) expression.level id1 gene1 liver batch1 0.67 id1 gene2 liver batch1 0.89 id2 gene1 kidney batch1 0.52 id2 gene2 kidney batch1...

...n matrix load("ratio_exp123.RData") #calculation correlation and p-values of genes using rcorr function y_cor_p123=rcorr(t(ratio_exp123),type="pearson") save(y_cor_p123,file="y_cor_p123.RData") diag(y_cor_p123$r)=0 #Selecting gene pairs with pcc value greater than 0.8 gene1=matrix(0,1,1) gene2=matrix(0,1,1) pcc=matrix(0,1,1) for(i in 1:nrow(y_cor_p123$r)) { for(j in 1:nrow(y_cor_p123$r)) { if(y_cor_p123$r[i,j]>0.8) { gene1=rbind(gene1,rownames(y_cor_p123$r[i,j,drop=F])) gene2=rbind(gene2,colnames(y_cor_p123$r[i,j,drop=F])) pcc=rbind(pcc,y_cor_p123$r[i,j]) } } } y_g...

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 following format: patientID gene1 gene2 gene3 gene4............

...t;control","drug","control"), example.df) > colnames(example.df) <- c(c("age","treatment"),paste("gene",1:4,sep="")) > rownames(example.df) <- paste("sample", 1:8, sep="") > example.df age treatment gene1 gene2 gene3 gene4 sample1 young drug 392 878 908 740 sample2 young control 167 263 711 392 sample3 young drug 155 252 242 547 sample4 young control 333 348 295 300 sample5 old drug 392 878 908 740 sample6 old control 167 263 711 392 sample7 old drug 155 252 242 547 sample8 old control 3...

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 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 1200 gene3 75 998 gene3 71 910 I have found one post w...

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 &g...

Hi. I have two vectors of gene expression for each of several days. I want to plot both vectors on the same plot for a visual representation of up versus down regulation. I've tried using add=T but that doesn't work. eg >plot(Day, gene1) >plot(Day, gene2, add=T) Any help would be appreciated. Iain

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 do that with the "merge" function but I need to make 2 "virtual" matrices, like this : &gt...

...(k) <- "kernel" data(promotergene) ind <- sample(1:dim(promotergene)[1],20) genetrain <- promotergene[-ind, -1] genetest <- promotergene[ind,-1 ] kx <- kernelMatrix(k, as.matrix(genetrain)) y<-as.vector(promotergene[-ind,1 ]) #y<-as.factor(promotergene[-ind,1 ]) y gene1 <- ksvm(kx, y, type="C-svc") gene1 genetype <- predict(gene1,genetest) Error en as.matrix(Z) : objeto "Z" no encontrado #genetest1<-as.matrix(genetest) #genetype <- predict(gene1,genetest1) genetype thank you, nelsonhernandez nhernandezgo en unal.edu.co

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 11 gene2 7.0 222 8 gene3 0.2 3 8 gene4 3.0 10 10...

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 chr1 30 40 11 + I just wondering is there an efficient way to find *overlapped, upstream and downstream genes for each gene in...

Dear Sir/Madam, I found your email address and your correspondence with R-users. I hope you could help me with this question about the function "ur.ers" in the package of "urca". It is an improved unit root test (Elliott et al. 1996 Econometrica). Do you know how to extract the value of the test statistic from the output? The only thing I can get is the print-out of all

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

...i]))!=0 | eval(as.symbol(colnames(dat)[j]))!=0) c <-cor.test(d[,1],d[,2]) cor.4gene[i*j,]<-c(colnames(dat)[i],colnames(dat)[j], c$estimate,c$p.value) } } cor.4gene<-as.data.frame(cor.4gene)%>%filter(V1 !=0) colnames(cor.4gene)<-c("gene1","gene2","cor","P") Can you tell me what mistakes I made? first, why cor is NA when calculation of correlation for g1 and g1, I though it should be 1. cor.4gene$cor[is.na(cor.4gene$cor)]<-1 cor.4gene$cor[is.na(cor.4gene$P)]<-0 cor.4gene.sq <-pivot_wider...

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 chr1 30 40 11 + I just wondering is there an efficient way to find overlapped, upstream and downstream genes for each gene in...