Preparing the Final Data Set

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1 Preparing the Final Data Set Kevin R. Coombes 17 March 2011 Contents 1 Executive Summary Introduction Aims/Objectives Methods Description of Data Statistical Methods Results Conclusions Details Load All Segment Information Appendix 5 1 Executive Summary 1.1 Introduction This report describes the analysis of a data set from Lynn Barron, a member of the laboratory of Lynne V. Abruzzo. This dataset was acquired using Illumina 610K SNP 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 thirteenth report in a series of related reports Aims/Objectives The objective of this report is to use the segment calls (as produced by Report 6) to produce a final data set containing the copy number and LOH status of each segment by patient and by chromosome. 1

2 13-finalDataSet Methods Description of Data The dataset contains measurements on 176 previously untreated patients with CLL. Extensive clinical followup is available Statistical Methods Raw data were processed in BeadStudio to yield genotype calls, log R ratios (LRR), and B allele frequencies (BAF) for each SNP in each patient. Since the study does not include matched normal DNA, the computations were performed relative to the pool of 120 HapMap samples run by Illumina. In Report 2, we applied the circular binary segmentation (CBS) algorithm to the intensity (log R ratio; LRR) data for each patient and each chromosome. CBS was first described by Olshan et al. [Biostatistics 2004; 23:657 63]; we use the implementation of CBS from the R package DNAcopy. In Report 3, we computed the odds ratio for LOH versus no LOH in windows of width 40 along each chromosome. In Report 4, we applied the CBS algorithm to transformed B allele frequency (BAF) values on each chromosome of each patient sample. In all three of those reports, we saved the segmentation results in per-patient files. In Report 6, we pooled the segment data from the different algorithms for each patient. We also computed summary statistics along each resulting segment, including the LRR mean and standard deviation, a summary of the genotpes for the SNPs in the region, and the best fit for modeling the BAF as a mixture of multiple components. In Reports 7 through 10, we assigned a meaningful call or interpretation to each segment. In this report, we combine all of these interpretations into the final data set. 1.3 Results We generate A COOL FILE Conclusions It would be nice to have some... 2 Details 2.1 Load All Segment Information We start by loading the segmentation data produced in Report 6. > load("rewind.rda") > shortnames <- sort(levels(rewind$samid)) We remove the sampels that failed QC or that should not be combined into the final data set.

3 13-finalDataSet 3 > removeme <- which(shortnames %in% c(.failedqc,.do.not.combine)) > if (length(removeme) > 0) { + combinenames <- shortnames[-removeme] else { + combinenames <- shortnames > save(combinenames, file = "combinenames.rda") > source("00rnw/snp-utils.r") Now we put the code in place to iterate over all chromosomes. In order to do this, we have to load the data for each chromosome (for at least one sample) in order to extract the positions of the SNP probes on the Illumina array. > curchr <- loadsnpdata(.egsample, chrname) > posn <- curchr$position Next, we read the positions and use the unique values from the segment data to indicate the start locations of pooled segments. > data <- rewind[rewind$chrom == chrname, ] > myholder <- data.frame(chrom = chrname, loc.start = sort(unique(data$loc.start))) Next, we compute the probe locations of the ends of the segments. > padded <- c(myholder$loc.start, 1 + posn[length(posn)]) > temp <- sapply(padded, function(i) { + n <- sum(posn < i) + ifelse(n < 0, NA, posn[n]) ) > myholder$loc.end <- temp[2:length(temp)] > rm(temp, padded) Then we count the number of SNP markers contained in each segment. > myholder$num.mark <- apply(myholder, 1, function(arow) { + sum((posn >= as.numeric(arow[2])) & (posn <= as.numeric(arow[3]))) ) The final step is to expand the calls for each patient from their own segments to the list of pooled segments > lev <- levels(rewind$call) > for (cid in combinenames) { + calls <- sapply(myholder$loc.start, function(loc, mydata) { + n <- sum(mydata$loc.start <= loc)

4 13-finalDataSet 4 + if (n < 1) { + warning("train") + return("nocall") + as.character(mydata[n, "Call"]), mydata = data[data$samid == cid, ]) + myholder[, cid] <- factor(calls, levels = lev) > if (!file.exists("final")) dir.create("final") Here is the main loop that puts the data set together. > memory.limit(2048) [1] > for (chrname in c(1:22, "X")) { + cat(paste("working on chromosome", chrname, "\n"), file = stderr()) + curchr <- loadsnpdata(.egsample, chrname) + posn <- curchr$position + data <- rewind[rewind$chrom == chrname, ] + myholder <- data.frame(chrom = chrname, loc.start = sort(unique(data$loc.start))) + padded <- c(myholder$loc.start, 1 + posn[length(posn)]) + temp <- sapply(padded, function(i) { + n <- sum(posn < i) + ifelse(n < 0, NA, posn[n]) ) + myholder$loc.end <- temp[2:length(temp)] + rm(temp, padded) + myholder$num.mark <- apply(myholder, 1, function(arow) { + sum((posn >= as.numeric(arow[2])) & (posn <= as.numeric(arow[3]))) ) + lev <- levels(rewind$call) + for (cid in combinenames) { + calls <- sapply(myholder$loc.start, function(loc, mydata) { + n <- sum(mydata$loc.start <= loc) + if (n < 1) { + warning("train") + return("nocall") + as.character(mydata[n, "Call"]), mydata = data[data$samid == cid, ]) + myholder[, cid] <- factor(calls, levels = lev)

5 13-finalDataSet 5 + write.table(myholder, file = file.path("final", paste("chr", chrname, + ".tsv", sep = "")), row.names = FALSE, col.names = TRUE, sep = "\t") + if (exists("finaldata")) { + finaldata <- rbind(finaldata, myholder) + else { + finaldata <- myholder > save(finaldata, file = "finaldata.rda") > write.table(finaldata, file = "finaldata.tsv", row.names = FALSE, col.names = TRUE, + sep = "\t") 3 Appendix This analysis was run in the following directory: > getwd() [1] "c:/mystuff/snp-cll/aa" 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() 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 loaded via a namespace (and not attached): [1] RColorBrewer_1.0-2