plotROC {rnaseqcomp} | R Documentation

## Estimate And Plot Differential Expression

### Description

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.

### Usage

    plotROC(dat, positive, fcsign, cut = 1, constant = 0.5,
      thresholds = seq(12, 0, len = 300), arrow = FALSE, ...)

### Arguments

`dat` | A `rnaseqcomp` S4 class object.
`positive` | A logical vector with length equivalent to row number of matrices in `dat@quantData`. `TRUE` means true differential and `FALSE` means true non-differential, while missing value `NA` means unknown.
`fcsign` | A numeric vector with length equivalent to row number of matrices in `dat@quantData`. Only values 1, -1, 0 are allowed. 1 means upregulated in second cell line, -1 means downregulated in second cell line, and 0 means no change. If elements in `fcsign` correspond to `NA` in `positive`, these elements will be ignored in estimation.
`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 `cut` in both samples, their fold changes will be set to 0. (default: 1)
`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`.

### Value

`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.

### Examples

    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)

[Package rnaseqcomp version 1.18.0 Index]