# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Options for reproducible research
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Use a fixed random seed? (if not, set to NULL)
fixedSeed <- 0xbeef;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Additional configurations
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
evalSignal <- "abs(fracB-1/2)";
addLegend <- TRUE;
addSdEst <- FALSE;
plotAllRocCurves <- c(TRUE, FALSE)[1];
plotTracks <- c(TRUE, FALSE)[1];
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Color settings
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
palette <- brewer.pal(n=9, name="Set1");
# Colors for different data sets/methods
colorMap <- c(
"*"="#000000",
"NA"="#999999",
"1"=palette[1],
"2"=palette[5],
"3"=palette[2],
"4"=palette[4]
);
# Colors for heterozygous and homozygous SNPs
hetCol <- "#000000";
homCol <- "#999999";
# Colors for full-resolution and smoothed signals
fullResColorMap <- c("*" = "#000000", "NA" = "#999999");
smoothedColorMap <- c("*" = "#6666FF", "NA" = "#999999");
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Options for (x, signal) plots
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
trackAspect <- 0.22;
trackWidth <- 0.9;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Options for (betaN, betaT) plots
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
## options for for plotsByState
addLinearRegressionLines <- TRUE;
addDiagHorizLines <- TRUE;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fixedNbrOfPoints <- 100;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Options for smoothing
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Bin by counts of genomic length?
byCount <- c(TRUE, FALSE)[1];
# Width of each bin
if (byCount) {
binWidths <- c(1, 2, 4);
binWidthS <- binWidths[length(binWidths)];
} else {
binWidths <- c(10, 50, 100)*1e3;
binWidthS <- binWidths[2];
}
binWidths <- binWidths;
# Add smoothed track?
addBinTrack <- TRUE;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Options for ROC analysis
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fpLim <- c(0, 0.6);
# Number of different ROC colors
rocCols <- 2;
robust <- c(FALSE, TRUE)[1];
robustStr <- ifelse(robust, "median", "mean");
binFFracB <- ifelse(robust, "median", "mean");
# Infer document tags
if (regexpr("ismpolish", dataSet) != -1) {
docTags <- "ismpolish";
genTags <- c("Birdseed", "NGC");
rocCurvesPattern <- "^(raw|TCN),Birdseed$|^TBN";
} else if (regexpr("(ACC,ra,-XY,BPN,-XY,AVG,FLN,-XY|CRMAv2)", dataSet) != -1) {
docTags <- gsub(".*,((ACC,ra,-XY,BPN,-XY,AVG,FLN,-XY|CRMAv2)(|-CalMaTe)(,[^,]+)*).*", "\\1", dataSet);
# docTags <- "CRMAv2";
genTags <- c("Birdseed", "NGC")[2];
rocCurvesPattern <- "^(raw|TCN),Birdseed$|^TBN";
} else if (regexpr("BeadStudio", dataSet) != -1) {
docTags <- gsub(".*,(BeadStudio(,[^,]+)*).*", "\\1", dataSet);
genTags <- c("BeadStudio", "NGC")[2];
if (confQuantile < 1) {
rocCurvesPattern <- "^(raw|TCN),NGC$|^TBN";
} else {
# The BeadStudio genotype calls contains 4% NC:s, which need
# to be excluded from the evaluation. However, if done, the
# comparison to naive genotype calls will no longer be objective.
# rocCurvesPattern <- "^(raw|TCN),BeadStudio$|^TBN";
rocCurvesPattern <- "^(raw|TCN),NGC$|^TBN";
}
} else {
throw("Cannot infer ('docTags', 'genTags') from 'dataSet': ", dataSet);
}
## if (regexpr("CalMaTe", dataSet) != -1) {
## docTags <- c(docTags, "CalMaTe");
## }
docTags <- c(docTags, tumorType);
genTag <- genTags[length(genTags)];
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