plotFC {rnaseqcomp} | R Documentation

## Estimate And Plot Fold Change Accuracy

### Description

For each pipeline, differential expression is estimated by fold change on mean signals across replicates of cell lines. For features that are truely differential expressed, their fold changes levels are summarized based on different levels of detrended logsignals.

### Usage

    plotFC(dat, positive, fcsign, constant = 0.5, loessspan = 1/3,
      thresholds = c(1, 6), ...)

### 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, NA 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` is NA or correspond to `NA` in `positive`, these elements will be ignored in estimation.
`constant` | A numeric constant that is added to quantifications before fold changes calculation. (default: 0.5)
`loessspan` | A numeric number indicating span used for loess smooth. Details see `loess.smooth` function. (Default: 1/3)
`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))
`...` | Parameters for base function `plot`.

### Value

`plot` | Fold change plots for all the quantification pipelines.
`list` | A list of two numeric vectors indicating median and standard error of fold changes in three different levels of detrended logsignals.

### 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)
    ## only select the true differential that have exact fold changes
    simdata$meta$fcsign[simdata$meta$fcstatus == "off.on"] <- NA
    plotFC(dat,simdata$meta$positive,simdata$meta$fcsign)

[Package rnaseqcomp version 1.18.0 Index]