Plot samples raw data and detected feature for a single ROI

plot a ROI across multiple samples (x axis is RT, y axis is intensity). If curveFit is provided, the fitted curve for each sample is added.

peakPantheR_plotEICFit(
    ROIDataPointSampleList,
    curveFitSampleList = NULL,
    rtMin = NULL,
    rtMax = NULL,
    sampling = 250,
    sampleColour = NULL,
    verbose = TRUE
)

Arguments

ROIDataPointSampleList

(list) list of data.frame of raw data points for each sample (retention time 'rt', mass 'mz' and intensity 'int' (as column) of each raw data points (as row)).

curveFitSampleList

(list) NULL or a list of peakPantheR_curveFit (or NA) for each sample

rtMin

(float) NULL or vector of detected peak minimum retention time (in sec)

rtMax

(float) NULL or vector of detected peak maximum retention time (in sec)

sampling

(int) Number of points to employ when plotting fittedCurve

sampleColour

(str) NULL or vector colour for each sample (same length as ROIDataPointSampleList, rtMin, rtMax)

verbose

(bool) if TRUE message when NA scans are removed

Value

Grob (ggplot object)

Examples

## Input data
# fake sample 1
# ROI data points
rt1             <- seq(990, 1010, by=20/250)
mz1             <- rep(522., length(rt1))
int1            <- (dnorm(rt1, mean=1000, sd=1.5) * 100) + 1
tmp_DataPoints1 <- data.frame(rt=rt1, mz=mz1, int=int1)
# fittedCurve
fit1        <- list(amplitude=37.068916502809756, center=999.3734222573454,
                    sigma=0.58493182568124724, gamma=0.090582029276037035,
                    fitStatus=2, curveModel='skewedGaussian')
class(fit1)     <- 'peakPantheR_curveFit'

# fake sample 2
# ROI data points
rt2             <- seq(990, 1010, by=20/250)
mz2             <- rep(522., length(rt2))
int2            <- (dnorm(rt2, mean=1002, sd=1.5) * 100) + 1
tmp_DataPoints2 <- data.frame(rt=rt2, mz=mz2, int=int2)
# fittedCurve
fit2        <- list(amplitude=37.073067416755556, center=1001.3736564832565,
                    sigma=0.58496485738212201, gamma=0.090553713725151905,
                    fitStatus=2, curveModel='skewedGaussian')
class(fit2)     <- 'peakPantheR_curveFit'

## Plot features in 1 sample without colours
peakPantheR_plotEICFit(ROIDataPointSampleList=list(tmp_DataPoints1),
                        curveFitSampleList=list(fit1),
                        rtMin=995., rtMax=1005.,
                        sampling=250, sampleColour=NULL, verbose=FALSE)


## Plot features in 2 samples with colours
peakPantheR_plotEICFit(
                ROIDataPointSampleList=list(tmp_DataPoints1,tmp_DataPoints2),
                curveFitSampleList=list(fit1, fit2),
                rtMin=c(995., 997.), rtMax=c(1005.,1007.),
                sampling=250, sampleColour=c('blue', 'red'), verbose=FALSE)