search for: methylated

Hi I am really sorry for this probably quite simple question. I am new to R, and I am running a pipeline that has already been made. All I have to do is give the paths for different folders, where the pipeline can find the files with my data. But every time I try to run the pipeline it returns with the message, that it cannot find the file. And I really don't know why. I have found the path

Hello, I have an input file: http://r.789695.n4.nabble.com/file/n3700031/testOut.txt testOut.txt where col 1 is chromosome, column2 is start of region, column 3 is end of region, column 4 and 5 is base position, column 6 is total reads, column 7 is methylation data, and column 8 is the strand. I would like a summary output file such as:

Hello, I have an R script that I use as a template to perform a task for multiple files (in this case, multiple chromosomes). What I would like to do is to utilize a simple loop to parse through each chromosome number so that I don't have to type the same code over and over again in the R console. I've tried using: for(i in 1:22){ etc.. } and replacing each chromosome number with

Dear List, I have been trying to use p.adjust() to do BH multiple test correction and have gotten some unexpected results. I thought that the equation for this was: pBH = p*n/i where p is the original p value, n is the number of tests and i is the rank of the p value. However when I try and recreate the corrected p from my most significant value it does not match up to the one computed by the

Hi, I want to use reutils to obtain the accession numbers of a query search in character format. When I use efetch, the accession number isn't in a character format, and I'm not sure if the number is accurate, because I get the error: Error in file.exists(destfile) : object 'destfile' not found This is what I tried: UIDs<-esearch( "Methylation" ) accession_numbers

Hi Bert and David, Thank you so much for willingness to spend some time on my problem!!! I have some statistical knowledge (going to get a master in applied statisitics), but do not have a chance to purse a phD for statistics, so I am always be careful before starting to do analysis and hope to gather supportive information from real statisticians. Sorry that I did not tell more info about

...rth highest significance INAS 0.107 0.115 0.123 0.115 0.166 0.000 MMA 0.091 0.107 0.115 0.142 0.179 0.000 DMA 0.126 0.103 0.126 0.129 0.142 0.031 The numbers in the dataframe indicate values of a continuous variable INMET (indicating ratio of inorganic arsenic vesus methylated arsenic species) for the respective quintiles of variables INAS, MMA, and DMA; "lowest" through "highest" indicate quintile levels of INAS, MMA, and DMA. INAS, MMA, and DMA are discrete ordinal variables (factors, each variable has following levels: lowest, ..., highest). Signi...

David: I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected: For example, if baseline control (no drugs) has a response of 0, drugA has an effect of 1, drugB has an effect of 2, and the effects are additive, with no noise we

Hello all, I would like to introduce our group's new bioinformatics package to you: pathfindR <https://cran.r-project.org/package=pathfindR> This tool is designed to improve pathway enrichment analysis by firstly identifying active subnetworksin differential expression/methylation data using a protein-protein interaction network. It then performs pathway enrichment analysis

I actually have two questions regarding the same script: ################################################# data <- vector('list', 24) splc <- vector('list',24) df.summ <- vector('list',24) for (i in 1:length(chrData)) { data[[i]] <- read.table(file=paste('chr',i,'.nonCG.covered.out',sep=''), header=F)

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Hello all, I would like to introduce our group's new bioinformatics package to you: pathfindR <https://cran.r-project.org/package=pathfindR> This tool is designed to improve pathway enrichment analysis by firstly identifying active subnetworksin differential expression/methylation data using a protein-protein interaction network. It then performs pathway enrichment analysis

Dear all, I try to use read.table to get the data from a tab delimited file, and some of the data is shown below: 3185 heterogeneous nuclear ribonucleoprotein F 3187 heterogeneous nuclear ribonucleoprotein H1 (H) 3188 heterogeneous nuclear ribonucleoprotein H2 (H') 3189 heterogeneous nuclear ribonucleoprotein H3 (2H9) 3190 heterogeneous nuclear ribonucleoprotein K ///

Hi I apologise for this question as it really must be a FAQ. Unfortunately, I can't find the answer and I'm tired of looking at endless google results A colleague of mine works for a state government department that has a policy against open source software or software tainted by open source. Other government departments in the same state use R but this particular department is driven

Dear all, Here is my code which am using to combine 5th column from different data sets. Here is the function to do my job genesymbol.append.file <-NULL gene.column <- NULL readGeneSymbol <- function(files,genesymbol.column=5){ for(i in fnames){ temp <- read.table(fnames,header=T,sep="\t",stringsAsFactors=F,quote="\"")

CRAN (and crantastic) updates this week New packages ------------ * bisectr (0.0.2) Maintainer: Winston Chang Author(s): Winston Chang <winston at stdout.org> License: GPL-2 http://crantastic.org/packages/bisectr Tools to find bad commits with git bisect * CUMP (1.0) Maintainer: Xuan Liu Author(s): Xuan Liu <liuxuan at bu.edu> and Qiong Yang <qyang at bu.edu>