abstract.tex

Spatial proteomics is the systematic study of protein sub-cellular
localisation. In this workflow, we describe the analysis of a typical
quantitative mass spectrometry-based spatial proteomics experiment
using the \Biocpkg{MSnbase} and \Biocpkg{pRoloc} Bioconductor package
suite. To walk the user through the computational pipeline, we use a
recently published experiment predicting protein sub-cellular
localisation in pluripotent embryonic mouse stem cells. We describe
the software infrastructure at hand, importing and processing data,
quality control, sub-cellular marker definition, visualisation and
interactive exploration. We then demonstrate the application and
interpretation of statistical learning methods, including novelty
detection using semi-supervised learning, classification, clustering
and transfer learning and conclude the pipeline with data export. The
workflow is aimed at beginners who are familiar with proteomics in
general and spatial proteomics in particular.