# Visualize distributional characteristics of spike-ins

Source: `R/Plot.R`
`plotSpike.Rd`

This function plots the results of the parameter estimation for spike-ins. This includes the absolute and relative sequencing depth (i.e. library size factor), a calibration curve as well as the capture efficiency given as a binomial regression.

    plotSpike(estSpike, Annot = TRUE)

## Arguments

estSpike | The output of `estimateSpike`.
Annot | A logical vector. If `TRUE`, a short figure legend is included.

## Value

A ggplot object.

## Examples

    if (FALSE) {
    # using example data set
    data("SCRBseq_SpikeIns_Read_Counts")
    data("SCRBseq_SpikeInfo")
    Batches = data.frame(Batch = sapply(strsplit(colnames(SCRBseq_SpikeIns_Read_Counts), "_"), "[[", 1),
                         stringsAsFactors = F,
                         row.names = colnames(SCRBseq_SpikeIns_Read_Counts))
    # estimation
    spikeparam <- estimateSpike(spikeData = SCRBseq_SpikeIns_Read_Counts,
                                 spikeInfo = SCRBseq_SpikeInfo,
                                 MeanFragLength = NULL,
                                 batchData = Batches,
                                 Normalisation = 'depth')
    # plotting
    plotSpike(estSpike = spikeparam, Annot = TRUE)
    }