Package: epiPortrait 0.99.3

Introduction | Design principles | A domain can change in different ways | Conceptual model | Relationship to existing methods | Installation | Quick Start with Example Data | Module 1: Building the Portrait Matrix | 1.1 The candidate-domain universe is required | 1.2 Consensus peaks from your peak-caller output | 1.3 Optionally stitch into macro-domains | 1.4 Build the portrait matrix | Custom features | Module 2: Normalization | Module 3: Super-Domain Calling | 3.1 Inflection methods | 3.2 log-transform is optional | 3.3 Modes | 3.4 Cutting on breadth: peak-level Breadth-Super | 3.5 Combined taxonomy | Module 4: Condition Transitions | Module 5: Annotation & Visualization | 5.1 Domain-aware annotation | 5.2 Hockey-stick plot | 5.3 Single-domain feature profile | 5.4 PCA and correlation QC | 5.5 Result visualizations | 5.6 Saving and reusing an analysis | 5.7 Differential domain analysis on continuous signal | 5.8 Score-aware BEDPE/3D annotation | 5.9 Per-sample time-course transitions | Module 6: Mark-Specific Interpretation | 6.1 H3K4me3: promoter breadth and transcriptional state | 6.2 H3K27ac: enhancer-domain magnitude versus regulatory territory | 6.3 H3K27me3 / H3K9me3: broad repressive domains | 6.4 Application patterns | 6.5 Cross-mark integration across domain universes | 6.6 Interpretation guardrails | 6.7 Input compatibility and parameter guidance | 6.7.1 Mark-aware preset workflow (e.g. H3K27me3) | 6.8 Parameter guidance | Troubleshooting | Session Info