Function Arguments and Tuning Parameters | Examples

SmCCNet overview | Workflow | SmCCNet package | SmCCNet workflow with a synthetic dataset | Synthetic dataset | Step I. Preprocessing | Step II: Determine optimal sparsity penalties through cross-validation (optional) | Create test and training data sets. | Set Scaling Factors | Run K-fold CV | Extract penalty pair with the smallest total prediction error | Step III: Run SmCCA with pre-selected penalty term | Step IV: Obtain multi-omics modules through network clustering | Step V: Obtain network summarization score and pruned subnetworks | Results | Step VI: Visualize network module | Methods for Optimal Scaling Factors Selection | Method 1: Use Prompt to Define Scaling Factors | Method 2: Use Automated SmCCNet to Select Scaling Factors Based on Pairwise Correlation. | Method 3: Use Cross-Validation to Select Scaling Factors | Cross validation with each set of scaling factors | Select optimal scaling factors with associated penalty parameters | Multi-Omics SmCCNet with Binary Phenotype | Extract penalty terms with the highest testing evaluation score | Step III: Run SPLSDA with pre-selected penalty terms | Acknowledgments | Session info | References

SmCCNet package | SmCCNet single-omics workflow with a synthetic dataset | Step I. Preprocessing | Step II: Determine optimal sparsity penalties through cross-validation (optional) | Create test and training data sets. | Run K-fold Cross-Validation | Extract penalty term with the smallest total prediction error | Step III: Run SmCCA with pre-selected penalty term | Step IV: Obtain single-omics modules through network clustering | Step V: Obtain network summarization score and pruned subnetworks | Results | Step VI: Visualize network module | Single-omics SmCCNet for Binary Phenotype | Import synthetic dataset with binary phenotype | Step II: Determine optimal sparsity penalty through cross-validation (optional) | Step III: Run SPLSDA with pre-selected penalty term | Session info | References