linear_quantlim: Calculation of the LOB and LOD with a linear fit

Description Usage Arguments Details Value Warning Author(s) References Examples

View source: R/linear_quantlim.R

Description

This function calculates the value of the LOB (limit of blank) and LOD (limit of detection) from the (Concentration, Intensity) spiked in data. The function also returns the values of the linear curve fit that allows it to be plotted. At least 2 blank samples (characterized by Intensity = 0) are required by this function which are used to calculate the background noise. The LOB is defined as the concentration at which the value of the linear fit is equal to the 95% upper bound of the noise. The LOD is the concentration at which the latter is equal to the 90% lower bound of the prediction interval (5% quantile) of the linear fit. A weighted linear fit is used with weights for every unique concentration proportional to the inverse of variance between replicates.

Usage

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linear_quantlim(datain, alpha = 0.05, Npoints = 100, Nbootstrap = 500)	

Arguments

datain

Data frame that contains the input data. The input 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)

alpha

Probability level to estimate the LOB/LOD

Npoints

Number of points to use to discretize the concentration line between 0 and the maximum spiked concentration

Nbootstrap

Number of bootstrap samples to use to calculate the prediction interval of the fit. This number has to be increased for very low alpha values or whenever very accurate assay characterization is required.

Details

Value

Warning

The LOB and LOD can only be calculated when more than 2 blank samples are included. The data should ideally be plotted using the companion function plot_quantlim to ensure that a linear fit is suited to the data.

Author(s)

Cyril Galitzine, Olga Vitek.

Maintainer: Cyril Galitzine (cyrildgg@gmail.com), Meena Choi (mnchoi67@gmail.com)

References

C. Galitzine et al. "Nonlinear regression improves accuracy of characterization of multiplexed mass spectrometric assays" Mol Cell Proteomics, doi:10.1074/mcp.RA117.000322, 2018.

Examples

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# Consider data from a spiked-in contained in an example dataset
head(SpikeInDataLinear)

## Not run: 
# Call function
linear_quantlim_out <- linear_quantlim(SpikeInDataLinear)

## End(Not run)

MSstats documentation built on Feb. 28, 2021, 2:01 a.m.