Preprocessing Large-Scale Proteomics Data

Recent advances in mass spectrometry instrumentation and computational analysis have established label-free data-independent acquisition (DIA) as a powerful tool in quantitative proteomics, offering depth, throughput, and reproducibility. These developments now enable large-scale projects with hundreds or even thousands of samples, opening new applications in systems biology and biomedicine. However, large-scale datasets require special consideration during data preprocessing. Here, we present a practical guide for preprocessing large-scale DIA datasets, illustrated using a public dataset. We cover key steps, including database searching, normalization, batch correction, missing value imputation, and evaluation of preprocessing strategies, and provide R code together with practical considerations.