Reorganizing the data by sample

Transcription

1 Reorganizing the data by sample Kevin R. Coombes 23 March 2011 Contents 1 Executive Summary Introduction Aims/Objectives Methods Description of Data Statistical Methods Results Conclusions Details 2 3 Appendix 4 1 Executive Summary 1.1 Introduction This report describes the analysis of a data set from HapMap, a member of the laboratory of HapMap. This dataset was acquired using Illumina 610K Quad v1 chips. The main goal of the study is to identify genetic abnormalities that are associated with clinical outcome (including overall survival and time-to-treatment). This is the zeroth part of a series of related reports Aims/Objectives There are two specific goals for the present report. First, we compute (if necessary) and record the desired sample names that will be attached to everything we do later. Second, we reconfigure the data (which starts out stored by chromosome) to store it instead by sample, which will make data handling much more efficient for all of the later steps. 1

2 00-dataDicer Methods Description of Data The dataset contains measurements on 225 normal controls. Extensive clinical followup is available Statistical Methods Raw data were processed in BeadStudio or GenomeStudio to yield genotype calls, log R ratios (LRR), and B allele frequencies (BAF) for each SNP in each sample. Since the study does not include matched normal DNA, the BeadStudio computations were performed relative to the pool of 120 HapMap samples run by Illumina. 1.3 Results The reconfigured data is stored in per-chromosome files within per-sample subdirectories in the ChrBySample subdirectory. 1.4 Conclusions We are ready to start our own processing of the data. 2 Details We need to run a double loop over chromosomes and samples in order to process all the data. We start with a block of code that loads the LRR and BAF data for one chromosome, which is denoted by the variable chrname. This code also extracts a list of sample names if one has not yet been created. > curdir <- paste("chr", chrname, sep = "_") > curchrfile <- file.path(.inputdir, paste("full Data Table_Chr_", chrname, + ".txt", sep = "")) > curchr <- read.table(curchrfile, sep = "\t", header = TRUE) > if (min(diff(curchr$position) < 0)) curchr <- curchr[order(curchr$position), + ] > if (!exists(".startcol")) { + g <- grep("gtype", colnames(curchr)) +.BS.COLS.PER.PT <- median(diff(g)) +.STARTCOL <- g[1] + rm(g) We use this code to load the data from chromosome 13. > memory.limit(2048)

3 00-dataDicer 3 [1] > chrname <-.CHRN Using the chromosome data, we can determine the short sample names. (This list may not be needed; it depends on how the raw image files were named when they were initially generated.) > shorten <- function(name) { + strsplit(name, "_")[[1]][2] > if (!exists("samplenames")) { + samplenames <- seq(.startcol, ncol(curchr),.bs.cols.per.pt) + shortnames <- names(samplenames) <- sub("\\.gtype", "", colnames(curchr)[samplenames]) + if (.MAKESHORTER) { + shortnames <- sapply(names(samplenames), shorten) + else if (exists("fixnames")) { + shortnames <- sapply(names(samplenames), fixnames) + else { + names(shortnames) <- shortnames + dup <- duplicated(shortnames) + if (any(dup)) + shortnames[dup] <- paste(shortnames[dup], "dup", sep = "-") > save(samplenames, shortnames, file = "allsamplenames.rda") We create a directory to store the results. > if (!file.exists("chrbysample")) dir.create("chrbysample") The main loop simply reads the data for each chromosome, parses it out by sample, and then appends the data for each sample to a separate file. > wext <- NULL > for (chrname in c(1:22, 'X', 'Y', 'XY', 'MT')) { + cat(paste("loading chromosome", chrname, "\n"), file=stderr())

4 00-dataDicer 4 + <<load.one.chromosome>> + for (i in 1:length(sampleNames)) { + longname <- names(shortnames)[i] + # key point: don't want to get prefixes + # without the final '.', looking for "CLZ.1" also gets "CLZ.11" + ln <- paste(longname, '\\.', sep='') + # but there is STILL a problem if one sample is called "X257" + # and another is called "X257.Rep2" + if (is.null(wext)) { + w0 <- which(regexpr(ln, colnames(curchr)) > 0) + wext <- sub(ln, "", colnames(curchr)[w0]) + targets <- c("name", "Chr", "Position", + paste(longname, wext, sep='.')) + cid <- shortnames[i] + cat(paste("\twriting", cid, "\n"), file=stderr()) + w <- which(colnames(curchr) %in% targets) + if (length(w) < 5) stop(paste("bad value, cid =", cid, "longname =", longname)) + if (length(w)!= 3+length(wext)) stop(paste("bad value, cid =", cid, "longname =", longna + # make sure a directory exists for this sample + samdir <- file.path("chrbysample", cid) + if (!file.exists(samdir)) dir.create(samdir) + # create by-sample file + fd <- file.path(samdir, paste("chr", chrname, ".tsv", sep="")) + write.table(curchr[,w], file=fd, quote=false, sep="\t", row.names=false) + rm(curchr) + gc() 3 Appendix This analysis was run in the following directory: > getwd() [1] "c:/snp-hapmap/analysis03" Note that \\mdadqsfs02 is the standard insititutional location for storing data and analyses; N: is the name given to that location on this machine. This analysis was run in the following software environment: > sessioninfo()

5 00-dataDicer 5 R version ( ) Platform: x86_64-pc-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C [5] LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grdevices utils datasets methods base