Summary | Prerequisites | Example I: TCGA glioblastoma dataset | Exploratory data analysis | Running M3C | Understanding M3C outputs | Visual check of consensus cluster structure | Example II: Regularised consensus clustering | Running regularised consensus clustering | Example III: Entropy objective function | Additional functions | Filtering features by variance | Closing comments | References

Contents | 1. Introduction | 2. Standard operation: single group | 3. Standard operation: multiple groups | 4. Running on Caret train output: single group, balanced data | 5. Running on Caret train output: multiple groups, balanced data | 6. Running on Caret train output: single group, imbalanced data | 7. Running on Caret train output: log-likelihood to select model | 8. Closing comments | 9. References

Contents | 1. Data types and requirements | 2. Quick start parameter settings | 3. Single-view clustering: Gaussian blobs | 4. Single-view clustering: Brain cancer RNA-seq | 5. Single-view clustering: Brain cancer RNA-seq clustering a range of K | 6. Multi-view clustering: Brain cancer multi-omics | 7. Multi-view clustering: Brain cancer multi-omics with missing data | 8. Single-view clustering: Non-Gaussian data, 3 circles | 9. Single-view clustering: Non-Gaussian data, spirals | 10. Ultra-fast single-view clustering: Gaussian blobs II | 11. Advanced operation: Ng spectral clustering | 12. Advanced operation: Customised data integration | 13. Parameter settings | 14. Code for heatmaps | 15. Closing comments | 16. References