plotROC {rnaseqcomp} | R Documentation |
For each pipeline, differential expression is first estimated by fold change on 1 vs. 1 comparison between cell lines. ROC curves then are made by comparing fold changes with predefined true differentials. Then, ROC curves from multiple 1 vs. 1 comparisons are averaged using threshold averaging strategy. Standardized partial area under the curve (pAUC) is reported for each pipeline.
plotROC(dat, positive, fcsign, cut = 1, constant = 0.5, thresholds = seq(12, 0, len = 300), arrow = FALSE, ...)
dat |
A |
positive |
A logical vector with length equivalent to row
number of matrices in |
fcsign |
A numeric vector with length equivalent to row
number of matrices in |
cut |
A numeric cutoff used to decide if fold change should be
estimated. For a 1 vs 1 comparison, if features have signals less than
|
constant |
A numeric constant that is added to quantifications before fold changes calculation. (default: 0.5) |
thresholds |
A numeric vector defining cutoffs on fold changes as the points to make threshold averaging on ROC curves. (default: seq(12, 0, len = 300)) |
arrow |
A logical indicating if error bars should be added to the averaged ROC curves. (default: FALSE) |
... |
Parameters for base function |
plot |
ROC plots for all the quantification pipelines. |
pAUC |
A numeric vector indicating pipeline accuracy. This is standardized partial AUC based on ranges chosen on false positive rate. |
data(simdata) condInfo <- factor(simdata$samp$condition) repInfo <- factor(simdata$samp$replicate) evaluationFeature <- rep(TRUE, nrow(simdata$meta)) calibrationFeature <- simdata$meta$house & simdata$meta$chr == 'chr1' unitReference <- 1 dat <- signalCalibrate(simdata$quant, condInfo, repInfo, evaluationFeature, calibrationFeature, unitReference, calibrationFeature2 = calibrationFeature) plotROC(dat,simdata$meta$positive,simdata$meta$fcsign)