Probabilistic Generation of Mass Spectrometry Molecular Abundance Variance for Case and Control Replicates

Shotgun differential mass spectrometry, the untargeted discovery of statistically significant differences between two or more samples, is a popular application with potential to advance biomarker detection, disease diagnostics, and other health objectives. Although many methods have been proposed, few have been quantitatively evaluated. The lack of ground truth data for shotgun difference detection limits quantitative evaluation and algorithmic advancement. While public mass-spectrometry data sets of single samples abound, data sets with more than one sample are rare, and data sets with the thousands of samples necessary to capture the complexity of real world populations are nonexistent due to technological and cost limitations. We present MSabundanceSIM, novel software for simulating any number of molecular samples based on one or a few real world data sets. The software uses a probabilistic model to generate case and control populations, with intuitive user parameters for tuning. We demonstrate variability by comparing to a real world data set over a range of abundances with differing biological and experimental variation coefficients. MSabundanceSIM is implemented in Ruby, is freely available, requires no external dependencies, and is suitable for a range of applications.