Nonlinear regression improves accuracy of characterization of multiplexed mass spectrometric assays [Research]

The need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Among many assay characteristics, the limit of blank (LOB) and limit of detection (LOD) are two particularly useful figures of merit. LOB and LOD are determined by repeatedly quantifying the observed intensities of peptides in samples with known peptide concentrations, and deriving an intensity versus concentration response curve. Most commonly, a weighted linear or logistic curve is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Here we argue that these methods inaccurately characterize assays where observed intensities level off at low concentrations, which is a common situation in multiplexed systems. This manuscript illustrates the deficiencies of these methods, and proposes an alternative approach based on non-linear regression that overcomes these inaccuracies. We evaluated the performance of the proposed method using computer simulations, and using eleven experimental datasets acquired in Data-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), and Selected Reaction Monitoring (SRM) mode. When the intensity levels off at low concentrations, the non-linear model changes the estimates of LOB/LOD upwards, in some datasets by 20-40%. In absence of a low concentration intensity leveling off, the estimates of LOB/LOD obtained with non-linear statistical modeling were identical to those of weighted linear regression. We implemented the non-linear regression approach in the open-source R-based software MSstats, and advocate its general use for characterization of mass spectrometry-based assays.

from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2EUlJTX
via IFTTT