Using a spike-in experiment to evaluate analysis of LC-MS data

The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose.The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.Changes in the abundance of particular proteins/peptides or their modifications can influence the state of an organism. The primary focus of biomarker discovery studies in proteomics is to study changes in peptide/protein abundances that are generated in response to a perturbation, disease, morphogenesis, toxicity, or other cell stress in a given biological system [1-4]. Liquid chromatography-mass spectrometry (LC-MS) has been an indispensable tool for profiling small differences in expression level of peptides/proteins in complex biological medium [5-8]. This is due to the development of soft ionization techniques and tandem mass spectrometry, which makes it a sensitive tool for detecting and identifying peptides. Data-dependent acquisition (DDA) or information-dependent acquisition (IDA) of tandem mass spectra has allowed complex p