Single-cell mass spectrometry-based proteomics (SCP) provides researchers with high-throughput protein quantification at the single cell level, which is revolutionising their view on complex biological processes, tissue heterogeneity and disease. Indeed, SCP has a pivotal advantage over single cell gene expression technologies by directly assessing proteins and their post-translational modifications (PTMs), key switches in many cellular pathways that play vital roles in cell proliferation, migration, metastasis and ageing. However, sensitive protein and PTM-level quantification at the single cell level is hampered by SCP data analyses that do not account for strong correlations in protein expression between cells of the same patient, nor for missing values that are omnipresent in SCP data, and, that are not tailored to PTMs. In this ambitious project we will therefore develop novel, cutting-edge differential data analysis solutions for single cell proteomics technologies that (1) correctly account for the correlation structure of SCP data so as to provide reproducible differentially abundant proteomics markers, (2) properly account for missing values, and (3) can discover differentially abundant PTMs from individual cells.