MSstats (version 3.4.0)

plot_quantlim: Plot of the curve used to calculate LOD and LOQ

Description

This function allows to plot the curve fit that is used to calculate the LOD and LOQ with functions nonlinear_quantlim() and linear_quantlim(). The function outputs for each calibration curve, two pdf files each containg one plot. On the first, designated by *_overall.pdf, the entire concentration range is plotted. On the second plot, designated by *_zoom.pdf,, the concentration range between 0 and xlim_plot (if specified in the argument of the function) is plotted. When no xlim_plot value is specified, the region close to LOD and LOQ is automatically plotted.

Usage

plot_quantlim(spikeindata, quantlim_out, dir_output, xlim_plot)

Arguments

spikeindata

Data frame that contains the experimental spiked in data. This data frame should be identical to that used as input by function functions nonlinear_quantlim() or linear_quantlim(). The data frame has to contain the following columns : CONCENTRATION, INTENSITY (both of which are measurements from the spiked in experiment) and NAME which designates the name of the assay (e.g. the name of the peptide or protein)

quantlim_out

Data frame that was output by functions nonlinear_quantlim() or linear_quantlim(). It contains the following columns: i) CONCENTRATION: Concentration values at which the value of the fit is calculated ii) MEAN: The value of the curve fit iii) LOW: The value of the lower bound of the 95% prediction interval iv) UP: The value of the upper bound of the 95% prediction interval v) LOD: The value of the LOD (one column with identical values) vi) LOQ: The value of the LOQ (one column with identical values) vii) NAME: The name of the assay (identical to that provided in the input) viii) METHOD which is LINEAR or NONLINEAR

dir_output

String containg the path of the directly where the pdf files of the plots should be output.

xlim_plot

Optional argument containing the maximum xaxis value of the zoom plot. When no value is specified, a suitable value close to LOQ is automatically chosen.

Warning

This plotting function should ideally be used every time nonlinear_quantlim() or linear_quantlim() are called to visually ensure that the fits and data are accurate.

Details

• spikeindata : Each line of the data frame contains one measurement from the spiked-in experiment. Multiple different INTENSITY values for the same CONCENTRATION are assumed to correspond to different replicates. Blank Samples are characterized by CONCENTRATION = 0.

• output : Each line of the data frame corresponds to a unique concentration value at which the value of the fit and prediction interval are evaluated. More unique concentrations values than in the input data frame are used to increase the accuracy of the LOD/Q calculations.

References

C. Galitzine et al. TBD 2016

Examples

# Consider data from a spiked-in contained in an example dataset. This dataset contains a significant
#threshold at low concentrations that is not well captured by a linear fit

head(SpikeInDataNonLinear)

#Call function
nonlinear_quantlim_out <- nonlinear_quantlim(SpikeInDataNonLinear)

#Get values of LOD/LOQ
nonlinear_quantlim_out$LOD[1]
nonlinear_quantlim_out$LOQ[1]

plot_quantlim(spikeindata = SpikeInDataLinear, quantlim_out  = nonlinear_quantlim_out,dir_output =  getwd())