Computational Methods and Algorithms for Mass-Spectrometry Based Differential Proteomics

摘要

As most high-throughput data, mass spec proteomics data are complex, noisy and incomplete. Additionally, in settings addressing questions about differential expression of proteins the data are usually represented by relatively few samples and a very large number of predictor variables, i.e., m/z peaks. These characteristics pose a significant challenge for most analysis methods. In addition, the preprocessing of the data remains an active research area having a great impact on the subsequent analysis steps.