analysing NimbleGen CGH for CNV
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@adeolu-adewoye-4377
Last seen 7.5 years ago
European Union
Dear All, I am interested in analysing NimbleGen aCGH for copy number variation using R packages. I have tried few of the available packages but having problem with processing Nimblegen .pair files as most of the packages only accept log2 ratio of Cy3/5 as an input. I managed to generate RGList object using RINGO package but got error messages when I tried to continue the analysis using snapCGH package. Please find below my session info: R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > setwd("/media/Data/Strains/test") > > library(limma) > library(DNAcopy) ********************************************************************** **** The plan to change the data format for CNA object has been postponed in order to ensure backward compatibility with older versions of DNAcopy ********************************************************************** **** > library(snapCGH) ###################################################################### ################ Have fun with GLAD For smoothing it is possible to use either the AWS algorithm (Polzehl and Spokoiny, 2002) or the HaarSeg algorithm (Ben-Yaacov and Eldar, Bioinformatics, 2008) If you use the package with AWS, please cite: Hupe et al. (Bioinformatics, 2004) and Polzehl and Spokoiny (2002) If you use the package with HaarSeg, please cite: Hupe et al. (Bioinformatics, 2004) and (Ben-Yaacov and Eldar, Bioinformatics, 2008) For fast computation it is recommanded to use the daglad function with smoothfunc=haarseg ###################################################################### ################ New options are available in daglad: see help for details. > library(Ringo) Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material. To view, type 'openVignette()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation(pkgname)'. Loading required package: RColorBrewer Loading required package: Matrix Loading required package: lattice Attaching package: 'Matrix' The following object(s) are masked from 'package:base': det Loading required package: grid > > list.files(pattern="pair.txt") [1] "27356202_532_pair.txt" "27356202_635_pair.txt" > head(read.delim(file.path("27356202_532_pair.txt"),skip=1))[,c(1,4:7 ,9)] IMAGE_ID PROBE_ID POSITION X Y SEQ_URL 1 27356202_532 RANDOM00060001 0 25 1 NA 2 27356202_532 RANDOM00060002 0 27 1 NA 3 27356202_532 RANDOM00060003 0 29 1 NA 4 27356202_532 RANDOM00060004 0 43 1 NA 5 27356202_532 RANDOM00060005 0 297 1 NA 6 27356202_532 RANDOM00060006 0 371 1 NA > read.delim(file.path("Targets.txt"), header=TRUE) SlideNumber FileNameCy3 FileNameCy5 Cy3 Cy5 1 1 27356202_532_pair.txt 27356202_635_pair.txt test ref > read.delim(file.path("SpotTypes.txt"), header=TRUE) SpotType GENE_EXPR_OPTION PROBE_ID Color 1 probe BLOCK* * blue 2 RANDOM RANDOM* * red > RG1 <- readNimblegen("Targets.txt","SpotTypes.txt") Reading targets file... Reading raw intensities... Read header information Read /media/Data/Strains/test/27356202_532_pair.txt Read /media/Data/Strains/test/27356202_635_pair.txt Determining probe categories... Matching patterns for: GENE_EXPR_OPTION PROBE_ID Found 2090470 probe Found 83156 RANDOM Setting attributes: values Color > head(RG1$R) 27356202_635_pair [1,] 268 [2,] 217 [3,] 182 [4,] 225 [5,] 7084 [6,] 207 > head(RG1$G) 27356202_532_pair [1,] 545 [2,] 645 [3,] 546 [4,] 589 [5,] 5974 [6,] 606 > > head(RG1$genes) GENE_EXPR_OPTION PROBE_ID POSITION X Y Status ID 1 RANDOM RANDOM00060001 0 25 1 RANDOM RANDOM00060001 2 RANDOM RANDOM00060002 0 27 1 RANDOM RANDOM00060002 3 RANDOM RANDOM00060003 0 29 1 RANDOM RANDOM00060003 4 RANDOM RANDOM00060004 0 43 1 RANDOM RANDOM00060004 5 RANDOM RANDOM00060005 0 297 1 RANDOM RANDOM00060005 6 RANDOM RANDOM00060006 0 371 1 RANDOM RANDOM00060006 > > RG1$targets SlideNumber FileNameCy3 FileNameCy5 Cy3 Cy5 1 1 27356202_532_pair.txt 27356202_635_pair.txt test ref > RG1 <- readPositionalInfo(RG1, source = "nimblegen") Error in data.frame(input$genes, Chr = as.numeric(chr), Start = (as.numeric(start)/1e+06), : arguments imply differing number of rows: 2173626, 0 > > RG1 <- read.clonesinfo("cloneinfo.txt", RG1) Error in data.frame(RG$genes, Position, Chr = as.numeric(Chr)) : arguments imply differing number of rows: 2173626, 0 > > RG1$printer <- getLayout(RG1$genes) Error in getLayout(RG1$genes) : gal needs to have columns Block, Row and Column I would appreciate suggestion on how to resolve this problem. In addition, if anyone has a better way of analysing NimbleGen aCGH for copy number, I am open to suggestions. Many thanks, Adeolu [[alternative HTML version deleted]]
aCGH probe aCGH snapCGH Ringo aCGH probe aCGH snapCGH Ringo • 818 views
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