search for: gened

I set up a shared mailbox with dovecot and it basically works. However, before I can imap SELECT or STATUS the shared mailbox I have to first do an imap LIST, i.e, . list "" * If list is not done first, the imap select or status command on the shared mailbox results in a "NO" response with "mailbox doesn't exist" response text. I can provide more

Good Afternoon, My name is Gabriel, I'm doing an analysis if there is increase or decrease in dependence on the mutated genes, using 3 or more genes using the fisher exact test.I performed with success an analysis for two genes using fisher.test( ). example of the 2x2 contigency table: Gene A mutated | Gene A normalGene B mutated| 26

Hi - When I'm trying to read in a text file into a labeled character array, the memory stamp/footprint of R will exceed 4 gigs or more. I've seen this behavior on Mac OS X, Linux for AMD_64 and X86_64., and the R versions are 2.4, 2.4 and 2.2, respectively. So, it would seem that this is platform and R version independant. The file that I'm reading contains the upstream regions

I would appreciate please a suggestion on how to do the following : i'm working with a dataframe in R that contains in a specific column multiple gene names, eg : > df.sample.gene[15:20,2:8] Chr Start End Ref Alt Func.refGene Gene.refGene284 chr2 16080996 16080996 C T ncRNA_exonic GACAT3448 chr2 113979920 113979920 C T ncRNA_exonic LINC01191,LOC100499194465

Hi Bogdan, Messy, and very specific to your problem: df.sample.gene<-read.table( text="Chr Start End Ref Alt Func.refGene Gene.refGene 284 chr2 16080996 16080996 C T ncRNA_exonic GACAT3 448 chr2 113979920 113979920 C T ncRNA_exonic LINC01191,LOC100499194 465 chr2 131279347 131279347 C G ncRNA_exonic LOC440910 525 chr2 223777758 223777758 T A

If row numbers can be dispensed with, then tidyr makes this easy with the unnest function: ##### library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(purrr)

Hello, I'm trying to add a column to the following data frame. The new column will contain "black" when the 5th column(if_TE_related) is "TE_related", or "orange" when the 4th column is " " (space). "chromo" "MSU_locus" "end5" "end3" "if_TE_related" "chr04" "LOC_Os04g01006" 1032

Hi all, I have a list of genes present in 500 individuals, the individuals are the elements: Genes <- lapply(1:nrow(inds),function(x) sample(1:10000,inds$No_of_Genes,replace=TRUE)) (This was later written to a dataframe as well as kept as the list object: inds2 <- data.frame(inds,Genes=I(Genes))) I also have a vector of how many of those genes are expressed in the individuals, this can

Hi, First my apologies for a non-working piece of code in a previous submission, I have corrected this error. I'm doing is individual based modelling of a pathogen and it's host. The way I've thought of doing this is with two dataframes, one of the pathogen and it's genes and effector genes, and one of the host and it's resistance genes. During the simulation, these things

Hello R-users, I am currently trying to learn how to use the function gls of the nlme library. I fitted the following model: Generalized least squares fit by REML Model: response ~ array + dye + genes + variety + variety * genes + array * genes + dye * genes Data: data I have 11 arrays, 2 dyes, 2 varieties, 3200 genes, and 2 replications for each. Therefore I should have the corresponding

X-Original-In-Reply-To: <CAD0Rxe=5uLCWQ+jfj1J6zepDzqx0vAiBREiwiKOih59MKgBDHg at mail.gmail.com> X-Previous: http://www.syslinux.org/archives/2015-January/023070.html On Mon, Jan 05, 2015 at 08:59:57PM -0500, Gene Cumm wrote: > On Sat, Jan 3, 2015 at 1:21 PM, Geert Stappers wrote: > > On Thu, Jan 01, 2015 at 09:48:16AM -0500, Gene Cumm wrote: > > >> I'm planning on

Hello R users, I have gene expression data of two groups of genes (large and small). Gene expression intensities of those genes are classified into 1 to 10 levels. What I want is to make a random set of genes that have the same levels as the small group from large group using sample(). I used smallvec to hold the number of genes in each levels (1 to 10) for small group, largevec for large group.

Hi BioC user, I have a problem extracting the gene set I would like to work with. Here is I work with my data: normData <- read.delim("normalizedData.txt",sep ="\t") ######### two class unpaired comparison # y must take values 1,2 classes <- c(-1,-2,1,2) #prepere the data for the samr analysis data.x <-as.matrix(normData[,8:11]) d=list(x=data.x,y=classes,

...0 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_nameupstream_genedownstream_geneoverlapped_gene gene1NAgene2NA gene2gene1gene4gene3 gene3gene1gene4gene2 gene4gene3gene5NA Currently , the strategy I use is like that, library(GenomicRanges) find_overlapped_gene <- function(idx, all_genes_gr) { #cat(idx, "\n") curr_gene <- all_genes_gr[idx]...

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

Hi, I'm heaving difficulties with a dataset containing gene names and positions of those genes. Not such a big problem, but each gene has multiple exons so it's hard to say where de gene starts and where it ends. I want the starting and ending position of each gene in my dataset. Attached is the dataset: http://www.nabble.com/file/p21312449/genlistchrompos.csv genlistchrompos.csv Column

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

Thanks for the advice. My question is more on how to do this? Let me use a biology gene analysis example to illustrate: In biology, there are always some house keeping genes which differ little even at pathological conditions. We know that at different batches, there are external factors affect the measurements. For example, overall signal intensity might be different due to lab reagents. A

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

Hi, all, I am new to R or even to statistics. Not sure if the question has a answer. But I couldn't find a straight forward answer in the help mailing list. I need use MicroArray data to select several diagnostic genes between Normal samples and Tumor samples and use these genes to predict unknow samples. Since the sample size is so small and data doesn't follow normal distribution, I am