search for: gene4

...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............ ------------------------------------------- 1 10 20 30 40 2 100 200 50 400 3...

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 ACGTGTGTACCCCC...

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 gene5 0.1 5 7 i want to plot column 1 versus...

...hole 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 the granges* For example, assuming all_genes_gr is a ~50000 genes genomic range, the result I want like belows: gen...

...hole 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 the granges For example, assuming all_genes_gr is a ~50000 genes genomic range, the result I want like belows: gene...

...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) colnames(test) <- c("Exp1","Exp2","Exp3","Exp4") rownames(test) <- c("Gene1","Gene2","Gene3", "Gene4") test <- as.table(test) mat = data.matrix(test) heatmap.2(mat, dendrogram="row", Rowv=TRUE, Colv=FALSE, distfun = function(x) dist(x,method = ''maximum''), hclustfun = function(x) hclust(x,method = ''centroid''), xlab = NULL, ylab = NU...

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 the "second principal axis" of this analysis? Aoife [[alternative HTML version deleted]]

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]]

...d not explained how log ratio will help me determine the significant value. GeneID treatment control treatment control treatment control Gene1 2.1 1 2 2.2 1.1 0.7 2.7 Gene2 1.5 1.4 1.7 2.2 1.3 1.2 Gene3 1.4 1.7 1.8 2.7 1.6 1.5 Gene4 2.2 2.4 2.1 2.3 2.1 1.9 Gene5 2.6 3.4 2.1 1.3 2.6 2.9 Objective: find genes who are differentially epxressed. -- View this message in context: http://www.nabble.com/Statistical-Questions%3A-finding-differentially-expressed-genes-tp15873163p15873163.h...

...ratio will help me determine the >significant value. >GeneID treatment control treatment control treatment control >Gene1 2.1 1 2 2.2 1.1 0.7 2.7 >Gene2 1.5 1.4 1.7 2.2 1.3 1.2 >Gene3 1.4 1.7 1.8 2.7 1.6 1.5 >Gene4 2.2 2.4 2.1 2.3 2.1 1.9 >Gene5 2.6 3.4 2.1 1.3 2.6 2.9 >Objective: find genes who are differentially epxressed. I'm not sure what you are asking, but to find whether one of your genes is significantly expressed is relatively straightforwa...

...> exprSet <- read.table('70mel_GSA.txt', row.names = 1,header =FALSE) > dim(exprSet) [1] 22277 71 > exprSet[1:4,1:4] V2 V3 V4 V5 GENE1 DDR1 10.215229 8.546666 9.207030 GENE2 RFC2 8.028489 8.175520 9.090902 GENE3 HSPA6 4.633769 4.822625 5.125172 GENE4 PAX8 6.121433 6.396281 6.000987 The two txt files are of similar format. The only difference so far I can tell is that the second file is of more rows and columns. Other than that, they are basically the same. But I don't know what is the issue with the first txt file. Thank you so much for...

...V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 gene1 0.14 0.07 -0.58 -0.56 -0.25 -0.17 1.02 0.98 0.18 0.28 0.23 0.37 gene2 NA NA NA NA NA NA NA NA NA NA NA NA gene3 0.00 0.28 -0.01 0.29 0.14 NA 0.23 NA 0.08 0.00 -0.47 -0.57 gene4 -0.58 -1.22 -0.43 -0.23 NA -0.36 0.30 0.28 0.30 0.41 0.33 -0.08 gene5 -1.51 -1.36 -1.64 -1.89 -1.32 -0.38 -0.14 -0.32 0.39 0.58 0.19 -0.40 gene6 -0.50 -0.60 -0.42 0.41 0.32 NA NA NA -0.69 0.29 0.12 0.11 > md.pattern(y) V2 V3 V4 V5 V10 V11 V12 V13 V14 V15 V16 V6 V8 V7 V9...

...uot;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 333 348 295 300 No...