A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data

Organelle proteomics, or spatial proteomics, is the systematic study of protein sub-cellular localisation. Here, we focus on high-throughput quantitative mass spectrometry-based techniques such as LOPIT and PCP and demonstrate a robust and sound analysis pipeline using state-of-the-art and novel machine learning algorithms implemented in the pRoloc R/Bioconductor package.