Assay characterization : estimation of limit of blanc (LoB) and limit of detection (LoD)

fig4_b The need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Although many assay characteristics exist, the limit of blank (LOB) and limit of detection (LOD) are particularly useful figures of merit. LOB and LOD are determined by repeatedly measuring the peak intensities of peptides in samples with known peptide concentrations, and deriving an intensity versus concentration response curve. Most commonly, a weighted linear regression is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Linear methods, however, inaccurately characterize assays containing a noise threshold at low concentrations, which is a very common situation. We propose a new approach based on non-linear regression that correctly captures the noise threshold. In absence of a noise threshold, the estimates of LOB/LOD obtained with non-linear statistical modeling are identical to those of weighted linear regression. However, in presence of a noise threshold the non-linear model changed the estimates of LOB/LOD by up to 20-40%. It improved the accuracy of the results, and avoided the unduly optimistic estimation of these figures of merit. We implemented the non-linear regression approach in the open-source R-based software MSstats, and advocate its general use for mass spectrometric protein assay characterization.

Documentation

User manual for LOB/LOD

Installation

Follow the installation of MSstats.

Maintainers

Cyril Galitzine, Northeastern University

Citing LOB/LOD analysis

  • C. Galitzine et al. “Nonlinear regression improves accuracy of characterization of multiplexed mass spectrometric assays.” Molecular & Cellular Proteomics (2018), doi:10.1074/mcp.RA117.000322