Introduction | Preparing the data | Primary data | Auxiliary data | The Gene Ontology | A note on reproducibility | The Human Protein Atlas | Protein-protein interactions | Support vector machine transfer learning | Nearest neighbour transfer learning | Optimal weights | Choosing weights | Applying best theta weights | References

Foreword | Questions and bugs | Introduction | Spatial proteomics | About R and pRoloc | Data structures | Example data | Importing and loading data | The original data file | From csv files to R data | A shorter input work flow | The MSnSet class | pRoloc's organelle markers | Data processing | Data visualisation | Profile plots | Sub-cellular cluster dendrogram | Average organelle class profile plot | Dimensionality reduction | Features of interest | Interactive visualisation | Assessing sub-cellular resolution | QSep metrics | Euclidian distance metrics | Data analysis | Unsupervised ML | Supervised ML | Classification algorithm parameters optimisation | Classification | Bayesian generative models | Conclusions | Acknowledgement | Session information | References

Foreword | Introduction | Getting started | Which app should I use? | The explore application | The compare application | The aggregate application | References

Introduction | Data sets | Other omics data | Unsupervised machine learning | Supervised machine learning | Algorithms used | Estimating algorithm parameters | Default analysis scheme | Parameter optimisation | Classification | Customising model parameters | Comparison of different classifiers | Bayesian generative models | Semi-supervised machine learning | Transfer learning | Session information | References