search for: methyl

...I really don't know why. I have found the path by going to the folder and finding the info about the folder, where the location of this is stated. I copy-paste this into the source command. The path for the folder is: When found with the "info about the folder": /Users/gban/Desktop/Methylation analysis/MethylationDataAnalysis But I think it should be: /Users/gban/Desktop/Methylation analysis/MethylationDataAnalysis/1.Normalization_raw_data/ as it is the folder 1.Normalization_raw_data where my data are in. I have tried both paths but it returns with this message every time: >...

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: http://r.789695.n4.nabble.com/file/n3700031/out.summary.txt out.summary.txt where column 1 is chromosome, column 2 is start of region, column 3 is end of region, column 4 is total reads in general, column 5 is t...

...ied in a general sense. ################################Code############################### chr2.data<-read.table(file="chr2.out.txt", header=F) colnames(chr2.data)<-c("chr","start","end","base1","base2","totalreads","methylation","strand") splc2<-split(chr2.data, paste(chr2.data$chr)) chr2.df<-as.data.frame(t(sapply(splc2, function(x) list(TR=NROW(x[['totalreads']]), RG1=sum(x[['totalreads']]>=1), percent=(NROW(x[['totalreads']]>=1)/sum(x[['totalreads']])))...

...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 method p.adjust: > setwd("C:/work/Methylation/IMA/GM/siteLists") > > hypTable <- read.delim("hypernormal vs others.txt") > pList <- hypTable$p > names(pList) <- hypTable$site > > adjusted <- p.adjust(pList, method="BH") > adjusted[1] cg27433479 0.05030589 > > pList[1]*nr...

...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 = efetch(UIDs, rettype = "acc") How do I obtain only the accession number and is the number accruate? Thank you [[alternative HTML version deleted]]

...analyze the data. There are nine dishes of cells. Three replicates for each treatment combination. So randomly select three dishes for no drug A/no drug B treatment, a second three dishes for drug A only, then last three dishes to add both A and B drugs. After drug treatments, they measure DNA methylation and genes or gene expression as outcome or response variables(two differnet types of response variables). My boss might want to find out net effect of drug B, but I think we can not exclude the confounding effect of drugA. For example, it is possible that drug B has no effect, only has effect...

...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). S...

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 (Over-Representation Analysis). By utilizing the interaction information, the tool identifies most of the involved pathways. pathfindR also creates pathway diagrams with the involved genes colored by...

...;), header=F) colnames(chrData[[i]])<-c("chr","start","end","tot","methy") splc[[i]]<-split(data[[i]], paste(data[[i]]$chr, data[[i]]$start, data[[i]]$end)) df.summ[[i]]<-as.data.frame(t(sapply(splc[[i]], function(x) summary(1-x$methylation)))) cat("finished reading",paste('chr',i),date(),"\n") } ################################################# 1) I'll start off first with perhaps the easier question. The above script is a portion of r code that I run from batch through a pipeline. I've de...

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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 (Over-Representation Analysis). By utilizing the interaction information, the tool identifies most of the involved pathways. pathfindR also creates pathway diagrams with the involved genes colored by...

...a protein 8 3313 heat shock 70kDa protein 9B (mortalin-2) 6302 sarcoma amplified sequence 6303 spermidine/spermine N1-acetyltransferase 6304 special AT-rich sequence binding protein 1 (binds to nuclear matrix/scaffold-associating DNA's) 6305 SET binding factor 1 6307 sterol-C4-methyl oxidase-like 6625 Small nuclear ribonucleoprotein 70kDa polypeptide (RNP antigen) 6626 small nuclear ribonucleoprotein polypeptide A 6627 small nuclear ribonucleoprotein polypeptide A' 6628 small nuclear ribonucleoprotein polypeptides B and B1 6629 Small nuclear ribonucleoprotein...

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>