Introduction | Installation | Simulated Multi-Omics Dataset | Module 1: Data Harmonization | Step 1.1 — Load host RNA-seq data | Step 1.2 — Load microbiome data | Step 1.3 — Match paired samples | Module 2: Joint Dimensionality Reduction | Module 3: Biomarker Discovery | Module 4: Diagnostic Classification | Full Pipeline with One Call | Module 5: Visualization | Joint integration biplot | Cross-omics biomarker network | Classifier comparison (bar chart) | Classifier comparison (ROC curves) | Feature flow (Sankey) diagram | Structured text report | Working with Real Data | HMP2 / IBDMDB dataset | Complex disease contexts | Design Principles | Why CLR for microbiome data? | Why DIABLO for integration? | Quantifying the multi-omics advantage | References | Session information

Introduction | The hidden confound in pseudo-bulk DE | Integration with Bioconductor workflows | The TC_ratio score | Installation | Simulated example: purely transcriptional signal | Running compoundDE | Inspecting results | Visualizations | Simulated example: purely compositional signal | Filtering by source | Recommended workflow | Session information

Introduction | The problem | What scBatchQC does | How scBatchQC compares to other QC packages | Installation | Quick start: simulate a two-batch dataset | Step 1: Batch-aware QC metrics | Understanding the key parameters | Step 2: Visualise QC distributions | Step 3: Estimate doublet rates | Step 4: Explore thresholds interactively | Step 5: Filter and continue | Storing results: BQCResult container | Edge cases | Recommended full workflow | Session information

Introduction | Installation | Quick start: unpaired design | Step 1: Filter sparse donors | Step 2: Inspect pseudo-bulk profiles | Step 3: Run differential expression | Step 4: Visualise results | The donor weighting advantage | Paired design support | Simulated paired design | Pseudo-bulk by donor × condition | Running fastDE on paired data | Why paired design matters | Session information