What geneticUMAP Does | Installation | Input Data | Run an Optimization | Result Files | References | Session Information

Overview | Read throughput on a single large BAM | End-to-end impact tracks the read fraction | A note on cores and fairness | Beyond reading: in-reader aggregation | Output is identical to the standard tools | Try it | Reproducing the full benchmarks | Session information

Introduction | The melanoma subset | Fitting with a condition | The responder block in the decomposition | Which pairs carry the condition difference | The genes that carry it | Session info

Overview | Example dataset | Input assay and transformation | Ordinary RPBC | Adaptive RPBC | Covariate-adjusted RPBC | Accessing harmonized values | Choosing an RPBC workflow | Additional fit information | Session information

1. Introduction | 2. Package Architecture and Workflow | Step 1: Input Real and Simulated Data | 2.1 Input Data Formats & Metadata Requirements (Cell Types & Batch Labels) | 2.2 Metric Measurement Capacity by Data & Metadata Mode | Step 2: Calculate Evaluation Measures | Step 3: Run the Complete Benchmark | Step 4: Review Scores, Rankings, and Figures | 3. Score Normalization and Visual Mapping Workflow | 3.1 Two-Step Score Normalization | 3.2 Visual Mapping to Bubble Matrices | 4. Installation and Getting Started | 5. Evaluating Data Properties and Distributions | 5.1 Comparative Distribution QC Plot (plot_distribution_qc) | 6. Evaluating Cell Clustering and Group Structure | 7. Evaluating Batch Effects and Technical Confounders | 8. Evaluating Cell Lineages and Developmental Trajectories | 9. Evaluating Biological Signals and Marker Genes (DEGs) | 10. Evaluating Computational Resource Usage (Scalability) | 11. Master Evaluation Pipeline: evaluate_multiomics_accuracy | 12. Visualization Suite | 12.1 Multi-Dimensional Benchmarking Bubble Matrix (plot_benchmark_bubble_matrix) | 12.2 Evaluation Summary Horizontal Bar Matrix (plot_evaluation_summary) | 12.2b Simulator Performance Leaderboard (compute_method_leaderboard) | 12.3 Metric Score Distributions Across Categories (plot_metric_boxplots) | 12.4 Comprehensive Benchmark Heatmap (plot_metric_heatmap) | 12.5 Dimension Reduction and Ordination Plots (PCA & MDS) | 12.6 Cell Embeddings (t-SNE, UMAP, & PCA) and Low-Dimensional Quality Metrics | 13. Multi-Dataset and Multi-Method Benchmarking | 14. Interactive Web Application (Shiny Studio) | 14.1 The 7 Modular Application Tabs | 15. Supported Multiomics Data Types and Metric Compatibility | 15.1 Three Experimental Designs | 15.2 Metric Compatibility by Data Type | 15.3 Recommended Functions by Data Type | 16. Large-Scale Multi-Dataset Benchmarking on HPC Clusters | 17. Extension to Other Single-Cell Data Types | 18. Summary | 19. Authors and Maintainers | 18. Session Information

Overview | Tab 1 — Home | Tab 2 — Data Hub | Subtab 1: Data Ingestion & Benchmark Studio | Mode 1 — Pre-Computed Benchmark (Demo Data) | Mode 2 — Single-Cell Evaluation (scRNA-seq / scATAC-seq) | Mode 3 — Multiomics Evaluation (scRNA-seq + scATAC-seq) | Metric Measurement Capacity by Upload Configuration | Mode 4 — Upload Pre-Computed Benchmark Table (.rds / .csv) | Subtab 2: View Uploaded Data & Matrices | Subtab 3: Dataset Properties Summary & HPC Cluster Execution | Tab 3 — Comparative Bubble Matrix & Leaderboard | Subtab 1: Comparative Bubble Matrix Figure | Subtab 2: Fidelity Leaderboard | Tab 4 — Visualizations | Tab 5 — Benchmark Results & Export | Interactive Master Benchmark Scores Table | Multi-Format Exports | Tab 6 — Help & Getting Started | Tab 7 — Contact | R API Cross-Reference

Interface | New species / speciesL argument | New version argument

Overview | Installation and loading | Discover supported attributes | Retrieve gene annotations | Retrieve transcript-level annotations | Use the getGene() compatibility wrapper | Retrieve sequences | Find orthologues | Migrating common r BiocStyle::Biocpkg("biomaRt") code | Roadmap and future direction | Session information

Introduction | Installation | Data | Reference: ImmGen stem and progenitor cells | Query: single-cell RNA-seq of stem and progenitor cells | Normalisation | Annotation | Results | Scores by FACS gate | Discrete labels | The phenotype space | Session information

Introduction | Installation | Data | Query: a Visium section of the mouse brain | Reference: single-cell RNA-seq of the mouse cortex and hippocampus | Annotation | Cell type maps | Niches | Session information

Introduction | Installation | The example data | Subsetting for this vignette | Creating a scProkaR object | Other entry points | Quality control | Integration across treatment groups | MNN correction | Harmony correction | Registering your own embedding | UMAP and clustering on the integrated space | Visualisation | Benchmarking the integration | Pseudobulk differential expression | Defining replicates | Aggregating and normalising | Pairwise contrasts | Time-course testing with splines | Where to go next | Further reading | Session information

Introduction | The problem TATA addresses | Relationship to Bioconductor single-cell infrastructure | Installation | Setting up | A simulated multi-drug branching time course | The TATA pipeline, step by step | Build the cell--cell graph | Cluster cells into coarse states | Score cluster pairs on topology and on time | Build the abstract graph | Compute pseudotime | The run_tata() wrapper | Comparing time-informed and topology-only runs | Benchmarking helpers | Temporal consistency plots | Branch probabilities and plasticity | Selecting cells for a branch | Branch-specific gene trends | Compact result overview | Practical guidance | Further reading | Session information

Introduction | Installation | Setup | Load Example Inputs | DMR Assembly from Seeds | DMR Summary | Motif Discovery | Motif-Based DMR Interactions | Visualizations | DMR Score Manhattan Plot | Single DMR Plot | Summary Circos Plot | CMEnt interactive Shiny App | Session Info

Overview of the Evaluation Architecture | Summary Table of the 8 Categories | Complete Master Reference Table (62 Metrics) | Category 1: Distribution Properties (14 Metrics) | 1. Kolmogorov-Smirnov (KS) Distance (KS) | 2. Wasserstein Metric (Earth Mover's Distance 1D) (Wasserstein) | 3. Median Absolute Deviation (MAD) | 4. Mean Absolute Error (MAE) | 5. Root Mean Squared Error (RMSE) | 6. Density Curve Overlap (OV) | 7. Bhattacharyya Distance (Bhattacharyya) | 8. Empirical Cumulative Distribution Area Difference (ECDF_DiffArea) | 9. Wald-Wolfowitz Runs Test Statistic (Runs_Statistic) | 10. 2D Fasano-Franceschini Test Statistic (Fasano_Franceschini_2D_KS) | 11. 2D Peacock Bivariate Test Statistic (Peacock_2D_KS) | 12. 2D Bivariate Kernel Density Estimation z-Statistic (KDE_Bivariate_zstat) | 13. Maximum Mean Discrepancy (MMD) | 14. Fréchet Single-Cell Distance (Frechet_SingleCell_Distance) | Category 2: Correlations and Zero Patterns (6 Metrics) | 15. Biological Coefficient of Variation Distance (bcv_discrepancy) | 16. Logistic Dropout Curve Midpoint Error (dropout_curve_midpoint_x0) | 17. Logistic Dropout Curve Slope Error (dropout_curve_slope_k) | 18. Mean-Variance Fit / Dropout Goodness-of-Fit (mean_variance_r2) | 19. Cell-Cell Pearson Correlation Distance (cell_pearson_cor) | 20. Feature/Gene-Gene Pearson Correlation Distance (feature_pearson_cor) | Category 3: Cell Structure and Clustering (10 Metrics) | 21. Average Silhouette Width Discrepancy (silhouette_sim) | 22. Dunn Index Discrepancy (dunn_sim) | 23. Davies-Bouldin Index Discrepancy (davies_bouldin_sim) | 24. Calinski-Harabasz Index Discrepancy (calinski_harabasz_sim) | 25. Adjusted Rand Index Concordance (ari) | 26. Normalized Mutual Information (nmi) | 27. Adjusted Mutual Information (ami) | 28. $V$-Measure (v_measure) | 29. Neighborhood Purity (neighborhood_purity) | 30. Generative Precision / Hungarian Optimal Matching (Generative_Precision) | Category 4: Batch Effects and Technical Confounders (7 Metrics) | 31. Batch Silhouette ASW (batch_silhouette) | 32. Batch Shannon Entropy (shannon_entropy) | 33. Principal Component Regression Batch Variance (pcr_r2) | 34. CellMixS Mixing Score (cms) | 35. Local Inverse Simpson Index (isi) | 36. Seurat Mixing Metric (seurat_mixing_metric) | 37. Local Density Factor Discrepancy (ldf_diff) | Category 5: Marker Genes and Biological Signals (15 Metrics) | 38. Symmetric Mean Absolute Percentage Error (SimBench_SMAPE) | 39. SimBench Composite DE Fidelity Score (SimBench_DE_Fidelity_Score) | 40. Log2 Fold-Change Pearson Correlation (Log2FC_Pearson_Corr) | 41. Log2 Fold-Change Spearman Correlation (Log2FC_Spearman_Corr) | 42. Top DEG Jaccard Overlap (Top_DEG_Jaccard_Overlap) | 43. DEG Detection Ratio (DEG_Ratio) | 44. Uniform P-Value Distribution Discrepancy (PValue_Uniformity_Chisq) | 45. Null P-Value Distribution Preservation Score (Distribution_Score) | 46. Machine Learning Cell Type Classification Accuracy (Classifier_Accuracy) | 47. Macro-Averaged F1 Score (Classifier_Macro_F1) | 48. Macro-Averaged Recall Score (Classifier_Macro_Recall) | 49. Shaky Foundations Group Silhouette Similarity (Silhouette_Sim) | 50. Shaky Foundations Silhouette Discrepancy (Silhouette_Discrepancy) | 51. Shaky Foundations Group PVE Similarity (PVE_Group_Sim) | 52. Shaky Foundations PVE Discrepancy (PVE_Discrepancy) | Category 6: Trajectory & Lineage Dynamics (2 Metrics) | 53. Pseudotime Rank Correlation (pseudotime_correlation) | 54. Lineage Tree Height Discrepancy (tree_height_rmse) | Category 7: Cross-Modal Coupling & Modularity (6 Metrics) | 55. Cross-Modal Cell Type Transfer Accuracy (cross_modal_accuracy) | 56. Cross-Modal Cell Type Transfer Macro-F1 Score (cross_modal_F1) | 57. Fraction of Samples Closer Than The True Match (FOSCTTM) | 58. Top-1 Cross-Modal Match Rate (match_at_1) | 59. Regulatory Coupling RV Coefficient (rv_coefficient) | 60. Cross-Modal Module Correlation Fidelity (module_correlation_r) | Category 8: Computational Scalability (2 Metrics) | 61. Wall-Clock Execution Time (elapsed_time_seconds) | 62. Peak Memory Allocation (peak_memory_mb) | Automated Score Normalization Pipeline

What path2path computes | Relation to the Bioconductor ecosystem | A five-minute tour on synthetic data | Real data | Two routes to significance | Session info | References

Overview | Load packages | Load example data | Prefilter genes | Fit TweedSpot | Tweedie index parameter | Extract and rank results | Visualize observed and fitted spatial patterns | Notes | Session information

Installation | Introduction | Visualization | Images | Labels | Points | Shapes | Layering | Scalebars | Session info

Purpose | Simulated protein abundances | Visualization of not normalized data | Visualization of missing data | Computing standard deviations, mean and CV. | Normalize protein intensities and show diagnostic plots | Fitting a linear model | Compute contrasts | Contrasts with missing value imputation | Gene Set Enrichment Analysis (GSEA) | Session Info | References

Introduction | Installation | Quick start | Further documentation | Session info | References

Introduction | Reading peaks | Chromosome naming | Call peaks permissively | Quick start | Weighting the replicates | Which weighting method to use | Choosing the combination method | How many replicates have to agree | Multiple testing | Calibrating the threshold | Input without statistics | Merging, and relative issues | Fixed-width regions | Handing over to a differential analysis | Blacklisting | Checking the result | Everything in one call | Session info

Introduction | Why CanvasXpressBio? | One dataset for every figure | A heatmap from a SummarizedExperiment | A volcano plot from differential-expression results | A Kaplan-Meier survival curve | A paired boxplot with per-patient connections | Session info

Introduction | Installation | Example data | Step 1: split dosages by local ancestry | Step 2: write per-ancestry GDS files | Step 3: ancestry-stratified gene tests | Further reading | Session information

Overview | Part A: Two populations | A.1 Unphased (dosage) split — split_diploid() | A.2 Phased (haplotype) split — split_haplotype() | Part B: More than two populations | B.1 Unphased (dosage) split — split_diploid_multi() | B.2 Phased (haplotype) split — split_haplotype_multi() | B.3 Four- and five-population extension | Part C: GLA shrinkage — when ambiguous carriers dominate | Session information

Overview | Part A: Two populations | Part B: More than two populations (K = 4) | Session information

Introduction | The model | Simulating a triplet | Fitting the model | Input from a SummarizedExperiment | Posterior summaries | Credible intervals | Sign probabilities of the interaction | Sign-reversal threshold | Effect curve | Choosing the chain length | Screening many triplets | Session information

Installation | Introduction | Relationship to other motif tools | Workflow overview | The background model | Obtaining a precomputed background | Preparing promoter sequences for pscan() | Scanning from a list of transcripts | Selecting promoters from a gene list | Configuring parallel execution | Data provenance | Session information

Installation | Introduction | The data | The background | From genes to promoters | A small executable scan | Running the scan | Checking the result against a second gene set | Comparison with the published figure | Inspecting the result object | Motif score patterns across promoters | Positional distribution of motif matches | Analysing a motif-selected promoter subset | Summary | Appendix: Comparison with the published analysis | Data provenance | Session information

Installation | Introduction | The data | The background | From genes to promoters | A small executable scan | Running the scan | Inspecting the result object | Motif score patterns across promoters | Positional distribution of motif matches | Analysing a motif-selected promoter subset | Summary | Appendix: Comparison with the published analysis | Data provenance | Session information

Introduction | Related work and scope | Installation | Aggregate single-cell counts | Construct an auditable example | Review the design before fitting | Fit and inspect the ledger | Output dictionary | Inspect figures | Report missing coverage, design failure and valid effects together | Reproduce a real single-nucleus example | Read the donor-deletion results | Method and data references | Session information

Introduction | Installation | Available resources | Retrieval through ExperimentHub | License and provenance | Session information

Introduction | Installation | An executable workflow | Table 1: Motif enrichment analysis and input preparation | Table 2: Background construction | Table 3: Result summaries and visualisation | Data provenance | Session information

Installation | Introduction | A small executable construction and round trip | Part A: Building a standard background | The promoters | The matrices | Building a standard background | Saving and reloading a standard background | Verification against the catalogue | Part B: Building and querying a full background | Saving and reloading a full background | Analysing a foreground by identifier | Part C: Checklist for a new background | Summary | Data provenance | Session information

title: "OmicsChrono: temporal metadata for longitudinal omics"author: "Mateo Pedersen"date: "r Sys.Date()"output: BiocStyle::html_documentvignette: >%\VignetteIndexEntry | Introduction | Relationship to existing Bioconductor software | Real RNA-seq time-course data | Civil timestamps and daylight saving | Zeitgeber time and photoperiod | Analysis-ready design data | Limitations | Session information

Introduction | Installation | Basic usage | Metadata-oriented BAM access | Filtering with ScanBamParam | Alignment-object output | Sequence and quality extraction | Fast count summaries | Relationship to existing Bioconductor tools | Session information

Installation | Introduction | Overview of the PACE workflow | The breast cancer dataset | Fitting the model | The pipeline step by step | Variance decomposition | Per-cell contamination | Pairwise spatial interactions | Macrophage–tumour drivers | Visualising the proximity effect | Biological interpretation | Saving a fit | Session info

Introduction | Installation | Create a heatmap object | Manual column ordering | Constructing from a SummarizedExperiment | Plot components | Compose and customize with patchwork | Subsetting and reclustering | Replacing assay values | Session information

What hcocena does | Installation | The example data | Setting up the analysis | Global settings | Layer settings | Part I: correlation and choosing a cutoff | Part II: group fold changes | Integration | Module detection | Functional enrichment | Characterising the modules | Which conditions drive a module | Correlating modules with metadata | Module GFC tables | Hub genes | Module scores | The object that carries all of this | Beyond this vignette | Citing hcocena | Session information

Introduction | Installation | Quick start | Details | Input data | Set chromosome order | Methods for the position of Bezier curve. | SessionInfo

Overview | Installation | Example data used in this vignette | Available motif sets | Binding sites: getTFbindsites() | GC frequency tables: getGCfreq() | Genome-wide GC distribution: getGenomeGC() | Using the annotations with methylTFR | Reading the real data from AnnotationHub | Cleaning up | Session info

Overview | Installation | Example data used in this vignette | Available motif sets | Binding sites: getTFbindsites() | GC frequency tables: getGCfreq() | Genome-wide GC distribution: getGenomeGC() | Using the annotations with methylTFR | Reading the real data from AnnotationHub | Cleaning up | Session info

Introduction | Installation | Example with a SingleCellExperiment | A small ligand-receptor table | Simulated data | One-call workflow | Step by step | Using CellChatDB | Statistical details | Performance | Session info

Introduction | Related packages | Installation | A self-contained example | Running mgcca | Individuals plot | Variables plot | Variance explained | Significant variables | The two plotting families | Saving and reloading results | How it scales to omics data | A live multi-omics example: miniACC | Assembling the tables | Choosing the penalty | Integrating the two layers | Reading the components | Are the components themselves significant? | Scoring individuals the fit never saw | Auditing the availability design before fitting | Auditing the object you are about to fit | Appendix: the full-scale TCGA walkthrough (not evaluated) | Download, impute and assemble the tables | Integrate the two layers | Do the shared components track the disease? | What drives the components | Subsetting by genomic ranges | Session information

Introduction | Reading the numbers without the algebra | Two words used throughout | What "alignment" actually means | The four numbers | In what order to look at things | What each combination means | Installation | A self-contained example | Does the omic signal know anything about my variable? | Which data type is the finding coming from? | When your study has batches, centres or stages | Is my result leaning on those groups? | Seeing it instead of inferring it | Which participants is it coming from? | Would I get the same answer with a different sample? | A result you can lean on | A result you cannot | What made the difference | Practical matters | Where the computation happens | Keeping your results | What you can and cannot claim | Session information

Introduction | Package functionality | Installation | Overview of the analytical workflow | Loading your data into myoRDNA | Step 1: Load the example methylation dataset | Step 2: Define experimental conditions | Step 3: Construct a methylRawList object | Step 4: Extract rDNA methylation data | Step 5: Adjust rDNA genomic coordinates | rDNA copy-number analysis | Absolute rDNA copy number | Calculating absolute copy number | Relative rDNA copy number | Calculating relative copy number | Considerations for copy-number estimation | Regional rDNA methylation analysis | Step 1: Partition rDNA into genomic regions | rDNA regions | Step 2: Calculate aggregate methylation metrics | Mathematical formulation of methylation proportions | Mean methylation proportion | Median methylation proportion | Differential rDNA methylation analysis | Statistical methodology | Binomial logistic regression | $$\operatorname{logit}(p_i) | \log\left(\frac{p_i}{1-p_i}\right) | $$\operatorname{Var}(Y_i) | Quasi-binomial logistic regression | $$\boxed{\operatorname{Var}(Y_i) | $$\widehat | Beta-binomial logistic regression | Step 1: Site-level differential methylation | Step 2: Region-level differential methylation | $$M_ | $$U_ | Step 3: Interpreting differential methylation | $$\widehat | $$\widehat | $$\boxed | 100\left(\widehat | Step 4: Multiple-testing correction | Beta-binomial optimisation and model diagnostics | Available optimisation algorithms | Assessing model convergence | Session information

Introduction | Installation | Getting Started | Read a Sample File | Annotation Resources | Input Data | Compute Deviation Score for a Single Sample and Single Motif | Run methylTFR on multiple samples and motifs | Run methylTFR directly on an RnBeads object | Session Information

Introduction | karioCaS and Bioconductor | Installation | Input data and project folder | Importing the reports | Analysis results: the kariocas_result object | Objective thresholds | Confidence Score: the Stability Index | Minimum reads: saturation analysis | The final mosaic | Visual exploration | Taxa persistence across CS | Relative abundance heatmaps | Taxonomic resolution | Core and unique taxa across samples | Acknowledgements | Session information

Introduction | Part 1: Understanding Multiomics Data Types | 1.1 Paired Multiomics (Co-assay) | 1.2 Unpaired Multiomics (Independent profiling) | 1.3 Mosaic Multiomics (Partial co-measurement) | 1.4 Metric Compatibility Summary | Part 2: Evaluating Paired Multiomics | 2.1 Loading the Example Paired Dataset | 2.2 Alignment in Joint Embedding Space (FOSCTTM & Match@1) | 2.3 Cross-Modal Cell Type Label Transfer | 2.4 Master Paired Pipeline: evaluate_multiomics_accuracy() | Part 3: Evaluating Unpaired Multiomics | 3.1 Step-by-Step Workflow for Unpaired Data | Part 4: Evaluating Mosaic Multiomics | 4.1 Step-by-Step Workflow for Mosaic Data | Part 5: Visualization | 5.1 Low-Dimensional Multiomics Cell Embeddings | Part 6: Summary

Overview | Bioconductor Motivation | Installation | Loading Packaged Example Data | Microbiome Relative Abundance Example | Single-Cell RNA-seq UMI Count Example | Single-Cell RNA-seq Non-UMI / Read Count Example | Pseudobulk Single-Cell RNA-seq Example | Bulk RNA-seq Count Example | Multi-omics Container | Session Info

Goal | Section A -- Cutoff sensitivity | A1. Trajectory classification cutoff (rho_cutoff) | A2. Three-axis weight sensitivity | Section B -- Tool comparison | Section C -- Negative control depletion | C1. Top-N depletion (negative controls absent from the most-prioritised candidates) | C2. Bottom-N enrichment (negative controls concentrated at the lowest-priority end) | Section D -- Held-out PDAC cohort projection (v0.99.12) | Section E -- Three-way baseline: where does enrichment come from? | Limitations of these analyses

Goal | 1. Why a hand-engineered rule (and not a supervised model) | 2. The 6-function API at a glance | 3. LTBP1 — the supported_uncertain exemplar | 4. GAPDH — the housekeeping negative control | 5. Anchor enrichment evaluation (post-freeze) | 6. Reading the case_study() output | 7. What the audit framework does not claim | Session info

Goal | Step 1 -- A simulated multi-cohort, multi-omic benchmark | Step 2 -- Depth-adjusted blood observability | Step 3 -- Rank candidates | Step 4 -- Does the ranking survive dropping a cohort? | Step 5 -- Comparators, scored honestly | Step 6 -- The frozen one-shot audit | Why a calibration check matters | Scope

Introduction | 1. Loading the Package and Example Dataset | 2. Step-by-Step Category Evaluation | 2.1 Distribution Properties & Summary Measures (Categories 1 and 2) | 2.2 Cell Clustering and Concordance (Category 3) | 2.3 Batch Effects and Confounder Mixing (Category 4) | 2.4 Differential Expression (DEG) Fidelity (Category 5) | 2.5 Trajectory and Lineage Dynamics (Category 6) | 3. Master Unimodal Pipeline: evaluate_simulation_accuracy | 4. Diagnostic Visualizations | 5. Conclusion

Introduction | 1. Loading the Packaged scATAC-seq Dataset | 2. Epigenomic Distribution & Summary Properties | 3. Epigenomic Cluster & Cell-Type Concordance | 4. Epigenomic Batch Integration & Confounder Handling | 5. Chromatin Peak Co-Accessibility & Regulatory Coupling | 6. Master Unimodal scATAC-seq Pipeline | 7. Conclusion

Goal | Layer 1 — Offline, bundled | Layer 2 — User cohort | Layer 3 — Re-derive the bundled atlas | Layer 4 — From FASTQ | Provenance summary | Session information

Goal | Side-by-side audit summary | Detailed audit features per gene | Case 1 — LGALS3BP (high_confidence anchor) | Case 2 — LTBP1 (supported_uncertain, Class B inverse exemplar) | Case 3 — ALB (penalized, plasma high-abundance gate) | Case 4 — GAPDH (excluded, housekeeping gate) | Take-away | Session information

Goal | Step 1 — Load the bundled toy cohort | Step 2 — Fit the stage-aware RNA model | Step 3 — Fit the protein limma model | Step 4 — Assemble the user evidence table | Step 5 — Run the audit framework on user evidence | Step 6 — Bioconductor-native input via SummarizedExperiment (v0.99.2) | Step 7 — Single-patient trajectory alignment (v0.4.1) | Scope boundary | Appendix A — Stage harmonization (TNM / AJCC -> Normal / Early / Mid / Late) | Appendix B -- Serum projection (binary group contrast, no stage)

Goal | Step 1 -- Build a synthetic non-PDAC cohort | Step 2 -- Project through the framework (one call) | Step 3 -- What changes between PDAC and non-PDAC? | Step 4 -- Translation discipline transfers, but the join target changes | Limitations

Introduction | From aGDS to annotation GDS files | Relation to other Bioconductor packages | Key functions | Installation | Inspecting an annotation GDS file | Annotating variants | From a character vector of variant IDs | From a data.frame | From a GRanges object | From a VCF file or a VCF object | Whole-genome annotation with one file per chromosome | Writing annotations into a SeqArray GDS file | Converting annotation sources to GDS | gnomAD | Ensembl VEP | SnpEff | Summarising annotations | Counting unique values | Summary report | Session information

Introduction | Installation | The four-state model | Example data and input format | SummarizedExperiment input | Validation | Fitting the full and null models | Inference: bootstrap test of sigma_c = 0 | Interpreting the results | Multiple testing | Exploratory ranking | Plots | Simulation | Operational-domain diagnostics | Limitations | Reproducibility and provenance | Session information

Introduction | Reading profiles | Alleles are stored pre-split | Marker names | Metadata | Scoring | Comparing end to end | Mixing | Writing results | Querying Cellosaurus | The command line application | The Shiny application | Session info

Overview | Data provenance & preprocessing | Load OmniCorr + example data | Very important: Sample alignment (required!) | Workflow steps | Step 1 – Feature clustering (using reference layer = Transcriptomics) | Step 2 – Transcriptomics reference heatmap | Step 3 – Cross-omics correlation analysis | Step 4 – Beautiful aligned correlation heatmaps | Nice combined layout (optional but very recommended) | Bonus: Correlation with metadata | Summary

Introduction | 1. Environment Setup and Data Preparation | 2. Importing MSigDB Signatures and Scoring | 3. Computing the Convergence Score | 4. Visualization | Proportion Barplot | Crossed Proportion Barplot | Density Distribution | 5. Statistical Testing | Bivariate Test | Multivariate Linear Model | Session Information

Introduction | Scope, and where this sits among related packages | Assumptions and limitations | How the package is organised | Installation | Quick start | Stage 1: data parse | The curated workbook | The TargetLynx export | The simulated dataset used from here on | Assembling a DatasetExperiment | Stage 2: peak-matrix processing | Blank filter | Detection filters | Batch correction | Normalise | Concentration adjustment | Impute missing values | The whole matrix in one call | Stage 3: QC check | How well was each compound measured? | Analytical drift | Normality | PCA | Loadings | What was reported, and what was dropped | Stage 4: statistics | Selecting the biological samples | Group summaries | Comparisons | Per-compound plots | Volcano | Heatmap | When the sample axis runs out | Feature correlations | Metabolite ratios | Driving it from a YAML config | Merging several acquisition panels | References | Session info

Build contract | Tested combinations | Linux CPU-only installation | Optional Linux performance configuration | macOS CPU-only installation | Windows CPU-only installation | Linux CUDA and cuVS installation | Audit the installed capability | Common failures | Checks and licenses | Session information

Introduction | Example data | Inspect execution capabilities | Run a nearest-neighbor search | Approximate search and observed recall | Automatic selection | Metrics and edge cases | Dense input contract | Reuse an index for prediction | Optional CUDA execution | Session information

One container, public accessors | Read a binned MSI table | Pixel and feature metadata | Coordinates and optical images | Paired modalities and standard subsetting | Optional Seurat boundary

Reference ownership and reproducibility | Build once, inspect before testing | Annotate experiment features without altering expression | Use the whole measured panel as the universe | One membership index for enrichment, expression and plots

Analysis questions determine the workflow | Supply modalities and study design explicitly | Compare representations using the same observations and features | Region characterization is richer than a mean difference | Keep biological-replicate inference separate | Ask whether colocalization survives regional composition | Map identities before pathway statistics | Run the real mouse-brain reference recipes | Render and retain a reviewable analysis

Membership and topology are separate resources | Identifier consolidation | Interaction correction | Loading and inspecting corrected graphs | Reproducing the correction

Clear responsibilities | Data to a native analysis container | Database discovery without network access | Annotate without inventing a downloaded database | Published native datasets and historical archives

Overview | RaMP snapshot and identifiers | A small 2-hydroxyglutarate example | From functional groups to ion-mode behaviour | Validated adduct rules | Build the reusable index | Chemical validity pruning | Query and rank candidates | Adduct-family support | Mass-defect heuristic | Annotate individual values or a SpaMTP object | Choose thresholds without re-annotation | Production use with the complete RaMP table | Carry the annotation threshold into real pathway analysis | Interpretation and limitations

Overview | Bioconductor container model | A binned MSI workflow | Alignment and pixel mapping | Binning and statistical reuse | Native feature plots and annotation ranking | Optional Seurat conversion boundary | Inspect available database resources | Create a small structure-aware database | Build and query an indexed search space | Reproducibility

Introduction | Creating a Parquet dataset | Accessing data | Filtering | Working with peaks | Controlling the dataset layout | Querying collections of mzPeak archives | How the dataset interfaces with mzPeak's model | Searching for peaks across runs | Sample metadata | The in-memory metadata cache | Streaming large mzML files | Performance | Saving and reloading the backend | Session information

Introduction | Acquiring and installing loopQTL and associated packages | System requirements | FIXME: update for 26/04 - recheck | Installation | Overview | loopQTLsetup: Validate inputs and build loopQTL object | loopQTLnormalize: Cross-normalization on Hi-C signal | loopQTLchooseK: Auto-select the number of PCs for covariates | runQTL: Anchor-restricted association testing | loopQTLpairData: Sample-level view of one loop × SNP pair | loopQTLannotate: Annotate results with target genes and regulatory peaks | loopQTLplotPair: Boxplot of loop strength by SNP dosage | loopQTLplotLocus: Locus-level track view of loops, SNPs, and genes | loopQTLplotSnpWindow: Hi-C-style beta heatmap around a SNP | Session info

Introduction | Installation | Input scope | Basic audit | Audit statuses | Reading a divergence | Reverse traversals | Digests | Walks and mappings | Comprehensive statistics | Diagnostics | Citation | References | Session information

Install cascadeR | Get example data | Inspect the metadata | Marker detection | Save results | Data Organization | First Run | Set up cascadeR analysis | Initial setup | General layout | Feature overview | sessionInfo

Overview | Installation | Preparing Your Data | Input format | Pre-processing recommendations | Quick Start | Simulate a three-modality dataset | Induce block-level missingness | Fit WOVEN | Exploring Results | Latent space scatter | VIP scores: which features matter most? | Feature loadings: direction of effect | Variance explained: choosing K | Quantitative metrics | Predicting New Subjects | Comparing WOVEN and DIABLO: Effective Sample Size | Accessing Raw Results | Session Info

Installation | Introduction | Input alignment | Collaborative graphical lasso | Comparing cell types | Exposure-restricted networks | Longitudinal networks | Mediation terminology | Composition and provenance | References | Session information

Installation | Introduction | Prepare a validated container | Fit the donor-pseudobulk model | Inspect results without changing the family | Sensitivity analyses | Composition is a different outcome | Legacy decomposition interfaces | Full reproducible analysis | Session information

Installation | Introduction | Unit of analysis and estimand | Required data contract | Canonical donor-pseudobulk model | Multiplicity and output interpretation | Repeated samples and composition | Developmental decomposition interfaces | Public source and reproducibility | Session information

Installation | Introduction | The problem | What this package provides | Relationship to existing tools | Why Bioconductor? | Quick start | Pipe-friendly workflow | Importing data | From VCF files | From iVar TSV | The WithinHostExperiment object | Quality control | Standard QC with flagISNV | Replicate-aware QC | Primer site filtering | Diversity analysis | Transmission analysis | Shared variant statistics | Quick bottleneck estimation | Interoperability | Convert to data.frame | Convert to VRanges | Export for downstream bottleneck tools | Visualisation | Reporting summaries | Case study: SARS-CoV-2 transmission pair | QC and diversity | Other pathogens | Influenza A (H3N2 HA segment) | HIV-1 (pol region) | Mycobacterium tuberculosis | Neutrality tests across pathogens | Advanced features | Neutrality tests | GFF3 gene annotation | Selection analysis | Within-host dN/dS | Selection coefficients from frequency trajectories | Multi-caller consensus | Exact bottleneck estimation | Longitudinal frequency tracking | SCI strand bias filtering | Adaptive technology-specific filtering | Diversity modelling and statistical utilities | Plausibility check | Scope and limitations | Session info

Installation | Introduction | Match each method to its estimand | Real-data complete-null diagnostics | A complete-null check in miniature | Simulation data-generating mechanisms | Metrics | A power approximation for planning | Comparator alignment | Multiplicity | Reproducibility record | Developmental ERD assessment | Session information

What problem does paweh solve? | Power and MSSN answer inverse questions | Five study-design families | Case-control association | Affected-child trios | Quantitative traits | Sequencing-based designs | LRTae validation/error-aware designs | Model-based and model-free inputs | Where should I start? | Why Bioconductor? | Published validation philosophy | Installation | Session information

Quantitative-trait study design in PAWEH | Genotype-specific trait distributions | Continuous-trait ANOVA design | Extreme-phenotype sampling | Multiple quantitative traits | Published-example reproduction: Gordon et al. (2017) | Joint multivariate threshold selection | Scope and limitations | References | Session information

What this package is for | A simulated two-array cohort | Probe-level effects, fitted within subject | Co-methylation clusters | Regions | Working from a SummarizedExperiment | Resampling subjects, not samples | Long-range blocks | Relationship to the pipeline and the Galaxy suite | Session info

What is LRTae? | Four validation tiers | A reproducible prospective design | Computing the NCP | Computing power | Computing minimum sample size | Why validation allocation matters | Reading the misclassification matrices | When to use these functions | References | Session information

From a scientific contrast to a design | Basic power and inverse design | Published-example reproduction: Gordon et al. (2002) | Extending the same design: how MSSN grows with DSB error | Published-example reproduction: Edwards et al. (2005) | Extending Edwards: prevalence and false-positive classification | Locus heterogeneity and the trend test | Differential genotype error: a Type I error warning | Independent sensitivity scenarios | Choosing an interface and recognizing limits | References | Session information

Why use an affected-child trio design? | Power and MSSN for trios | Model-based and model-free TDT inputs | Chapter 5 genotype misclassification | Published-example reproduction: Buyske et al. (2009) | Extending Buyske: a misclassification curve | Extending Buyske: prevalence by misclassification | Published-example reproduction: Chen et al. (2009) | Extending Chen: dilution by heterogeneous trios | Assumptions and limitations | References | Session information

Introduction | Launching the dashboard | Case-Control workflow | TDT / Family workflow | Quantitative Trait workflow | Full continuous trait | Extreme phenotype sampling | Multiple quantitative traits | Detailed panels in Case-Control and TDT | Reproduce calculations in R | Frequently asked questions | Dashboard and programmatic workflows | Further reading | Session information

What tewas is for | A synthetic array with planted loci | The linear scan: tewas_v16() | The pieces, one at a time | A declared design: tewas_frame() | Bioconductor classes: SummarizedExperiment in, GRanges out | The report: tewas_report() | Target genes: tewas_genes() | The standing of each arm | Session information

Introduction | Installation | Multithreading | Example analysis on data by Vicari et al. (2024) | Data exploration | Single-image analysis | Multi-image analysis | Other common input formats | SpatialExperiment and MultiAssayExperiment | Single-image | Multi-image | AnnData | Troubleshooting | Sbivar takes forever | Job did not deliver a result | Warnings | Variogram fitting | GAM fitting | gfortran not found on Mac | Session info | Bibliography

Overview | Mathematical Methods and Implementations | Singular Value Decomposition Algorithms | Partial Least Squares Algorithms | Classification Methods | Computational Backends | Backend and CPU Configuration | Supported Computational Routes | Public API and Data Inputs | Bioconductor Data Containers | Choosing a Starting Model | Classification Tasks | Fit And Predict A Classifier | Evaluate Classification Predictions | Classification Heads | Kernel PLS For Classification | Classification Score Plots | Regression Tasks | Fit And Predict A Regression Model | Float32 Input | Evaluate Regression Predictions | OPLS For Regression | Cross-Validation | Ordinary k-fold CV | Grouped k-fold CV with constrain | Leave-one-out CV and leave-one-group-out CV | Component and hyperparameter optimization CV | Interpreting R2Y, Q2Y, and RMSD | Permutation-test p-values | Nested or double CV | SVD Utility | CUDA and Metal Availability | Helper Functions | Detailed Algorithmic Notes | Singular-vector backends | PLS-SVD | SIMPLS | OPLS | Kernel PLS | Classification heads | Method References | Session Information

Introduction | Where the resources are stored | Inspecting resources without downloading | Retrieving a resource | Retrieving a coherent bundle | Working from a staged directory | The three-package workflow | Recording the version used | Session information

Introduction | Installation | Cell Death Gene Sets | Available Gene Sets | Representative Gene Sets | Pathway Selection | Global analysis | Recommended representative pathway | All gene sets within a cell death type | Exact gene set name | Workflow Overview | Input Data Specification | Single-group ORA | Single-group GSEA | Multi-group ORA | Multi-group GSEA | ssGSEA | Minimal Working Examples | ssGSEA and Differential Analysis | Exporting Results | Data Sources and Limitations | Session Information

Install TBB using brew | Install RcppParallel with TBB path | Session info

Introduction | Installation | A stash for MsExperiment objects | Creating self-contained stashes | Retrieve MS experiments from MetaboLights | Session information

Introduction | Why scGenePanel in Bioconductor? | Features of scGenePanel visual | Installation | Data included in this package | Load data | Create a multi-panel plot | Session information

Overview | Two sequencing workflows in paweh | Exact published TDT1-NGS replication: the Kim AIS example | TDT1-NGS power and MSSN | TDT sequencing sensitivity | Published case-control framework: the Ahn example | The PAWEH CC-NGS sequencing bridge | CC-NGS power and MSSN | Separate case/control sequencing | Why zero per-read error is not perfect at finite depth | CC sequencing sensitivity | CC locus heterogeneity | The exact pi = 0 boundary | Fixed-depth assumptions and limitations | A practical design workflow | References | Session information

Introduction | Installation | Glossary | PipelineTemplate | PipelineDemo | General overview of MagellanNTK | Data format | Modules in MagellanNTK | History | Folder and files configuration | config.txt | General settings | Generic functions | Other informations | R | md | Code Structure of a Shiny Module for MagellanNTK | PipelineName.R file | PipelineName_conf() | PipelineName_ui() and PipelineName_server() | PipelineName_Description.R file | PipelineName_Description_conf() | PipelineName_Description_ui() and PipelineName_Description_server() | PipelineName_Description_server() detailled explanation | PipelineName_Process1.R and PipelineName_Process2.R files | PipelineName_Process1_conf() | PipelineName_Process1_ui() and PipelineName_Process1_server() | PipelineName_Process1_server() detailled explanation | PipelineName_Process1_server() Description sub-step | PipelineName_Process1_server() Sub-step 1 sub-step | PipelineName_Process1_server() Save sub-step | PipelineName_Save.R file | PipelineName_Save_conf() | PipelineName_Save_ui() and PipelineName_Save_server() | PipelineName_Save_server() detailled explanation | PipelineName_Convert.R file | PipelineName_Convert_conf() | PipelineName_Convert_ui() and PipelineName_Convert_server() | PipelineName_Convert_server() detailled explanation | PipelineName_Convert_server() Description sub-step | PipelineName_Convert_server() Convert sub-step 1 sub-step | PipelineName_Convert_server() Save sub-step | Launch the pipeline | Session information

Introduction | Objectives of the vignette | Intuitive overview of the PipelineDemo app | Installation | MagellanNTK jargon | Objects | Timelines | Navigation | Data format requirements | General user interface | Main sidebar | Main interface | Process sidebar | Workflow header | Content area | Step-by-step discovery of PipelineDemo | Launch the pipeline | Open a dataset | Exploratory Data Analyzer (EDA) | Workflow | 'Description' step | 'DataGeneration' step | 'Preprocessing' step | 'Clustering' step | 'Save' step | Other parts of the pipeline | 'Convert' process | 'Save as' tab | 'Manual', 'FAQ' and 'Release Notes' tabs | Other functionalities | Resetting a step | Running a single process | Session information

Introduction | Installation | Accessing the resources | Data availability | Session information

Pipeline Workflow & Decision Points | Step 1: Input Validation | Step 2: Handling Missing Metadata (Optional) | Step 3: Differential Abundance Analysis | Step 4: Confounder-Adjusted Interaction Inference | Step 5: Visualize Taxa-Pathway Interactions | Optional: global interaction correction

Introduction | Installation | The sample annotation | A simulated batch effect | Step 1: is the design suitable for batch correction? | Step 2: how much variance does the batch explain? | Why the default hides it, and what to do | Step 3: correct, and keep the biology | Protecting a variable inside ComBat | Step 4: verify | Did the batch effect go? | Did the biology survive? | What it changed in the results | Removing two factors, one after the other | Inspecting the correction with PCA | Applying the workflow to a real batch effect | The batch has to be declared, not discovered | The decomposition finds it | Correcting it, and checking against biology | Visual assessment with PCA | Statistical significance and fold-change magnitude | References | Session information

Introduction | Installation | Example data | How missing values are represented | Filtering, log2 transformation and normalisation | Imputation of missing values | The workflow in a single call | Where the processed data are stored | Differential abundance results | Relationship with the missing values | Detection and non-detection patterns between conditions | Visualising the results | Interactive figures | Static figure | Next steps | Session information

Introduction | Installation | The experiment | Declaring the truth | Scoring a pipeline | The metrics_table summary | How TP, FP, TN and FN are defined | The confusion plots: the whole picture at once | Species-level confusion plot | Overall confusion plot | The metrics heatmap | The OpDEA metrics | Interpreting ROC curves | The full ROC curve | Zooming into the low-FPR region | Comparing search engines across FPR ranges | Volcano plots as diagnostic tools | Comparing multiple pipelines | Comparing TP, FP, TN and FN | Ranking pipelines with the OpDEA criteria | Comparing saved results and external pipelines | What this benchmark can and cannot show | References | Session information

Introduction | Installation and packages | Normalisation | What normalisation does | Normalisation methods available in NADIA | How normalisation methods are evaluated | Comparing normalisation methods with NADIA | Imputation | Why values are missing | How NADIA represents missing intensities | Imputation methods available in NADIA | How imputation methods are evaluated | Comparing imputation methods with NADIA | Applying the selected combination | Effect on differential abundance results | Practical decision guide | References and software | Session information

Introduction | DIA: Spectronaut | DIA: DIA-NN | Declaring the experimental design | TMT: Proteome Discoverer | Label-free: Proteome Discoverer | Where the design comes from | Naming the conditions | Four sources, one common object | Session information

Introduction | Installation | Why values are missing | How NADIA represents missing intensities | Diagnosing missingness in the example data | Data processing shapes the missing-value pattern | Imputation strategies available in NADIA | Why one method may not represent every gap | The default hybrid: combo | softHybrid: a continuous combination | limpa: detection-aware estimates with uncertainty | Choosing among the strategies | References and software | Session information

Introduction | Setup and example data | Running Pattern Profiler | Selecting proteins for clustering | Choosing the number of clusters | Understanding the soft-clustering output | Visualising the expression patterns | Protein profiles within each cluster | Comparing cluster centroids | Saving and reusing the results | Saving the long-format table as Parquet | Storing Pattern Profiler in a NADIA project | Practical recommendations | References | Session information

Introduction | Prepare an example analysis | Install and load NADIA | Run the processing workflow | Choose an output for the task | Export tabular results | Choose between TSV and Parquet | Create a set of TSV and/or Parquet files | Archive a complete analysis | Write and read a .nadia file | Restore and verify the result | Inspect stored tables and provenance | Query or extract the archive | Combine differential abundance with clustering | Explore results with interactive tables | Save interactive HTML | Save a complete widget page | Save an individual widget as one file | Preserve reproducibility | References | Session information

Introduction | Setup and example data | Installation | Processing the example dataset | Choosing the appropriate visualisation | Interactive and static output | Highcharts licence | Volcano plots | Summarising the selected comparison | Creating and reading the plot | Labelling selected proteins and multiple comparisons | Boxplots of sample distributions | Comparing assays numerically | Creating and reading the boxplot | Principal component analysis | Comparing protein subsets | Creating and reading the PCA plot | Subset modes, ellipses, and sample filtering | Heatmaps | Selecting proteins for the heatmap | Creating and reading a focused heatmap | Annotations and repeated heatmaps | Protein pattern clusters | Building and summarising the example clusters | Comparing cluster centroids | Inspecting representative protein profiles | Colours and consistent presentation | Exporting figures and data | Interactive charts | Static heatmaps | Practical recommendations | Session information

Introduction | Installation | Loading the package | Retrieve the TxDb object from AnnotationHub | Package summary | Chromosome information | Available chromosomes | Gene annotations | Transcript annotations | Exon annotations | Coding sequence (CDS) annotations | Promoter regions | Transcripts grouped by gene | Exons grouped by transcript | Session information

Introduction | Installation | Load required packages | Retrieve the TxDb object from AnnotationHub | TxDb summary | Sequence information | Gene annotations | Transcript annotations | Exon annotations | Coding sequences | Promoter regions | Transcripts grouped by gene | Exons grouped by transcript | Gene ranges | Session information

Introduction | Installation | Function Parameters | Core Input Parameters | beta | seeds | pheno | Sample Grouping Parameters | sample_group_col | casecontrol_col | ignored_sample_groups | Array and Genome Parameters | array | genome | Filtering Parameters | ext_site_delta_beta | min_seeds | min_adj_seeds | min_sites | Region Building Parameters | max_lookup_dist | expansion_window | max_bridge_seeds_gaps | max_bridge_extension_gaps | Statistical Parameters | max_pval | entanglement | testing_mode | empirical_strategy | ntries | mid_p | aggfun | Performance Parameters | njobs | verbose | Input/Output Parameters | seeds_id_col | output_prefix | beta_row_names_file | BED File Parameters | bed_provided | bed_chrom_col | bed_start_col | Annotation Parameters | annotate_with_genes | score_dmrs | Advanced Parameters | .load_debug | Global Package Options | Parallelism | Option: CMEnt.njobs | Verbosity | Option: CMEnt.verbose | Memory Management | Option: CMEnt.beta_in_mem_threshold_mb | Caching | Option: CMEnt.use_annotation_cache | Option: CMEnt.annotation_cache_dir | Option: CMEnt.jaspar_cache_dir | Motif Analysis | Option: CMEnt.jaspar_version | Option: CMEnt.jaspar_tax_group | Option: CMEnt.min_motif_similarity | Option: CMEnt.jaspar_corr_threshold | Option: CMEnt.make_debug_dir | DMR scoring | Option: CMEnt.scoring_nfold | Configuration Examples | Example 1: High-Confidence DMRs with Strict Filtering | Example 2: Broad Region Detection with Relaxed Parameters | Example 3: Empirical P-values for Small Sample Sizes | Best Practices | Troubleshooting | Issue: Out of Memory Errors | Issue: DMRs Too Small | Issue: Too Many DMRs | Issue: Slow Performance | Session Info

Installation | Background | Overview | Reference signatures | How it works | Preparing input data | Load reference expression data | Choosing a full reference database | Map the query to the reference | Starting from a table or CSV file | Calculating regulator scores | Checking a known target | Using an explicit gene set | Visualise the results | Inspecting a single reference signature | Where the perturbed gene ranks: target_gene_percentile | Filtering on the target percentile | Functional enrichment of the regulator hits | Scoring methods available | Comparing scoring methods | Interpreting the score, zscore, and overlap columns | Tuning topN | Parallelisation | Verifying hits before interpretation | Saving results to a file | Related packages | Citation | Session information | Acknowledgements

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

Why strainspy? | Load package and example data | Choosing key wrapper inputs | Fit the main model (glmZiBFit) | Find top hits | Add taxonomy context | Generate plot-ready outputs | Diversity and prediction preparation | Working with other profile inputs | Notes | Next steps | Session information

Introduction | Installation | Load GeneTrackR | Locate the bundled example data | Read the three core inputs | Inspect a candidate gene | Retrieve variants for the same locus | Build gene haplotypes | Test a phenotype association | Where to go next | Session information

Introduction | Installation | File inventory | Core dimensions | Coordinate conventions | Load representative objects | GeneA haplotype truth | Phenotype truth and controls | LD truth | RNA-seq and Ribo-seq signal truth | Recommended use in examples and tests | Session information

Introduction | Installation | Load the canonical demo inputs | Object contracts used in the workflow | Annotation and feature retrieval | Variant retrieval | GeneA haplotypes | Haplotype-phenotype association | Single-variant phenotype association | Linkage disequilibrium | Phenotype-guided haplotype refinement | Variant-effect prioritization | Session information

Introduction | Installation | Supported formats and coordinate conventions | Load the deterministic annotation files | Annotation object contract | Validate and summarize annotations | Retrieve genes, transcripts, and regions | Exact gene or transcript retrieval | Genomic-region retrieval | Retrieve a flat table | Convert annotations and expose standardized tables | Merge compatible annotations | Write annotations and perform round-trip checks | GenePred / GenePredExt | GTF / GFF3 | BED6 / BED12 | Visualize annotation structure | Gene model | Transcript structure | Genomic region | Interval feature track | Gene-length distribution | Recommended annotation workflow | Session information

Introduction | Installation | Plot a gene | Transcript collapse modes | Plot a transcript | Genomic coordinates | Spliced transcript coordinates | Plot a genomic region | Strand direction | CDS, UTR, and exon geometry | Labels | Highlight an interval | Colors and borders | Choosing the structural view | Session information

Introduction | Installation | Read signal tracks | Lazy versus memory mode | Strand semantics | Retrieve a genomic interval | Bioconductor interoperability | Normalize and merge signals | Bin retrieved signal | Write bigWig output | Plot signal over a gene | Plot signal over a transcript | Ribo-seq reading-frame view | Plot a genomic region | Signal summaries and visual transformations | Integrated track figure | Practical guidance for large signal data | Session information

Introduction | Installation | Read variants | Large indexed VCF files | Validate and summarize | Retrieve by genomic region | Retrieve by gene or transcript | Filter by variant attributes | Plot a variant track | Write a standardized VCF subset | Downstream handoff | Session information

Introduction | Installation | Prepare the track objects | Choose exactly one genomic locator | Gene-centered view | Transcript-centered view and Ribo-seq frames | Explicit multi-gene region | Add feature and variant panels | Multiple independent feature or variant tracks | Layout and panel heights | Styling responsibilities | Large-dataset workflow | Session information

Introduction | Installation | Step 1. Prepare VCF genotypes and annotation | Step 2. Build the primary GeneA haplotypes | Step 3. Inspect the HapVariant object | Step 4. Choose the genotype representation | Allele-string mode | Binary code mode | Step 5. Control missing-genotype filtering | Step 6. Construct transcript, flanking-region, and custom-region haplotypes | Transcript-defined haplotypes | Strand-aware flanking regions | Explicit genomic regions | Sample and variant-type filtering | Step 7. Draw the haplotype-variant figure | Step 8. Pass the same haplotype object downstream | Session information

Introduction | Installation | Step 1. Prepare phenotype and genotype inputs | Step 2. Summarize phenotype quality and distributions | Step 3. Verify sample matching explicitly | Step 4. Test the primary haplotype-phenotype association | Step 5. Compare traits and choose the statistical settings | Step 6. Control phenotype-association plot appearance | Step 7. Test a single variant against phenotype | Step 8. Pass the same objects downstream | Session information

Introduction | Installation | Step 1. Prepare VCF genotypes and annotation | Step 2. Compute the primary twelve-variant LD-gradient example | Step 3. Inspect the LDTrack object and pairwise statistics | Step 4. Inspect the LD matrix and quantify the LD gradient | Step 5. Draw the triangular LD heatmap | Step 6. Add gene structure and natural-variant markers | Step 7. Compare the LD-gradient, low-LD, and two-variant regions | Step 8. Control samples, variant types, LD methods, and genotype retention | Select samples | Select variant types | Retain the dosage matrix when needed | Use D-prime cautiously | Step 9. Interpret LD and pass the selected region downstream | Session information

Introduction | Installation | Step 1. Prepare the original haplotypes and phenotype data | Step 2. Inspect genotype similarity and phenotype correspondence before refinement | Step 3. Refine haplotypes using protein_content | Step 4. Inspect the HapRefined object | Step 5. Inspect pairwise tests and merge decisions | Step 6. Plot the refined haplotype structure | Keep the original haplotypes inside refined blocks | Collapse each refined group to one row | Step 7. Plot phenotype distributions after refinement | Step 8. Evaluate trait choice and refinement thresholds | A strongly haplotype-associated trait may preserve all groups | A negative-control trait can collapse all groups | Multiple traits make the refinement criterion stricter | Step 9. Interpret and reuse refined haplotypes | Session information

Introduction | Installation | Step 1. Prepare GeneA variants and phenotype data | Step 2. Understand genotype coding before interpreting effect direction | Step 3. Calculate the primary protein-content effect scan | Step 4. Inspect the result object | Step 5. Rank candidate variants | Step 6. Interpret tied effects together with genotype structure and LD | Step 7. Inspect signed effects carefully | Step 8. Validate varA03 with a direct single-variant phenotype plot | Step 9. Compare multiple traits and connect downstream evidence | Session information

Introduction | Step 1. Define and read the inputs | Step 2. Validate and summarize the core objects | Step 3. Inspect the locus in a browser-like view | Step 4. Retrieve the gene-body variants | Step 5. Construct and visualize the GeneA haplotypes | Step 6. Test phenotype associations | Step 7. Inspect the regional LD gradient | Step 8. Refine haplotypes with phenotype evidence | Step 9. Rank variant effects and integrate the evidence | Step 10. Export results and scale the workflow to real datasets | Installation | Session information

Introduction | Step 1. Prepare output directories and representative objects | Step 2. Export genomic track files | Annotation tracks | Signal tracks | Variant tracks | Step 3. Save figures as PDF and PNG | Step 4. Export analysis tables | Step 5. Export the LD matrix cleanly | Step 6. Optionally collect tables in an Excel workbook | Step 7. Save complete GeneTrackR objects with RDS | Step 8. Record provenance and verify the export | Installation | Session information

What is served | Local mode | Provenance | Session info

The problem | One call | The two columns that matter | Every target gene, one row per pair | Abstention is deliberate | Checking a result that looks one-sided | The limitation that governs everything | Session info

1. Import data | 2. Set analysis parameters | 3. Assign gRNAs to cells (optional) | 4. Run quality control (optional) | 5. Run calibration check | 6. Run power check (optional) | 7. Run discovery analysis | 8. Write outputs to directory | Further reading

Overview | Select the backend and CPU core count | A first analysis | A bundled Bioconductor expression dataset | Parameter philosophy and intended scope | Choosing between UMAP and t-SNE | Optional downstream graph clustering | PCA and reproducible t-SNE initialization | Reusing a nearest-neighbor graph | Independent UMAP initialization | UMAP graph modes | Float32 and memory-conscious analysis | Embedding new observations | Landmarking | Measuring embedding quality | CPU, Metal, and CUDA | Package boundary | UMAP implementation | Interpolation-based t-SNE implementation | Graph clustering and landmarking | Backend reporting and software acknowledgements | A practical analysis checklist | References

Introduction | Citation | Gene-environment interaction analysis | Data expectations | RAMEN's underlying assumptions | Genomic data | DNA methylation data | Exposome data | Covariates to include | Workflow overview | Install RAMEN and set up environment | Identify VML and summarize their methylation state | Note on argument: var_method | Note on argument: var_distribution | Running RAMEN::findVML() | Identify cis SNPs | Conduct variable selection on genome and exposome variables | Author's note about variables interpretation | Identify the best explanatory model (G/E/G+E/GxE) per VML | Fit and compare the models and select the best one | Remove poor performing winning models | Author's note about model interpretation | Variations to the standard workflow | Frequently Asked Questions | How can I save the RAMEN data frames that have columns with lists as observations? | How can I set up the parallel backend? | Session info

Introduction | The example dataset | Accessing the two assays | 1. Dimensionality reduction | 2. Differential TF activity | Ranking motifs by variability | Testing between two groups | Choosing an effect-size threshold | Visualising the differential motifs | 3. Comparing the two compartments | 4. Motif footprints | Observed and expected profiles | Bias-corrected footprint | References | Session Information

Overview | Workflow | 1. Loading the data | 2. Setup the tTEscanR object | 2.1 Pre-processing | 2.2. Defining the tTEscanR object | 3. Standard workflow | 3.1. Codon usage assessment | 3.2. Anticodon usage assessment | 3.3. Amino acid level assessment | 3.4. Theoretical Translation Efficiency (tTE) computation | 4. Differential expression analysis | 5. References

1. Overview | 2. Obtaining the tRNA matrix | 3. Identifying the optimal tRNA cutoff

Overview

1. Overview | 2. Configuration options | 2.1. Data transformation | 2.2. Parameters | 3. Visualization options | 4. References

Introduction | 1. Setup and Mock Data Generation | 2. Data Aggregation | 3. Extracting specific Metrics and Quality Control | 4. Dimensionality Reduction | 5. Differential Epigenetic Analysis | Session Information

Introduction | 1. Prepare Mock Multi-Omics Data | 1.1 Global Metadata | 1.2 Region Dictionary | 1.3 Simulate Omics Matrices | 2. Multi-Omics Integration | Mode A: Tri-Omics Integration (RNA + CpG + GpC) | Mode B: Dual Integration (RNA + CpG) | Mode C: Epigenetics Only Integration (CpG + GpC) | Session Information

Introduction | 1. Data Preparation | 2. Object Construction and QC | 3. Dimensionality Reduction | 4. Unsupervised Clustering | 5. Differential Expression Analysis (DEA) | 6. Trajectory Inference (Pseudotime) | Session Information

Introduction | 1. Single-Cell RNA Visualization | 1.1 Quality Control Plot | 1.2 Dimensionality Reduction Plot | 1.3 Volcano Plot for DEA | 1.4 Trajectory & Pseudotime Plot | 2. Epigenetic Visualization | 2.1 Epigenetic Landscape (Ridge Plot) | 2.2 Dimensionality Reduction for Epigenetics | 2.3 Epigenetic Volcano Plot | 3. Multi-Omics Integration Plots | 3.1 Omics Correlation Matrix | 3.2 Pairwise Scatter Plots | Session Information

The adapter in one example | The recipes | Does the pipeline choice change the answer? A real-data check | Session info

Introduction | Installation | Data Preparation | Statistical Model | Empirical-Bayes Precision Shrinkage | Fixed-Effects Analysis | Longitudinal Analysis (Mixed-Effects) | Parameter Estimates and Result Interpretation | Top-ranked CpG sites | Visualization | Session Info

The problem this package exists to solve | What a direction-blind test sees | The alignment step | Running the three lenses | Which lens fires is itself a result | What DMSA does not do | Moving to real data

Complete Execution Run | Session Information

DgeaHeatmap | Installation | Usage | Differential Gene Expression Analysis Using Nanostring Data

Overview | Select default execution resources | Basic workflow | Bioconductor objects | Migration from KODAMA 2.4 | Innovations | Renamed or deprecated interfaces | Session information

Introduction | Installation | Quick start | Other ways of creating sets | KEGG | Custom gene sets | Comparison of phase distributions of sets under two conditions | Identification of sets that do not follow the experimental phase distribution | Contribution of each individual element to the complete phase distribution of the set | References

Function names, signatures, and return values | FAQ | Why a new package?

Why circaTime | The data | From raw to phased: estimatePhase() | Not sure which engine to pick? method = "consensus" | Gene identifiers across ID types and species | How good is this? Benchmarking against known ground truth | Confidence intervals and quality flags | Handing off to downstream rhythmicity analysis | Is circadian time confounding my design? | What isn't covered yet | Session info

Introduction | Installation | Workflow | Preparation | Load Data | Data Cleaning and Transformation | Unsupervised Analysis | Supervised Analysis | Session Info

Introduction | Installation | What diagnosis checks | A minimal example | Instrument strength | Negative-control validity | Proceeding without instruments or controls | Further reading | Session information

Introduction | Installation | Exposure instruments from GWAS summary statistics | Quality-control the summary statistics | Build a polygenic score with LDpred2 | Score a panel of pre-computed weights | Verify instrument strength | Mediator instruments | Scan for cis-eQTLs | Build a genetically predicted mediator (GReX-style) | Negative controls from high-dimensional panels | Methylation beta to M-values | Residualise on covariates | Extract negative-control factors with PCA | Apply FUSION-style TWAS weights | Importing a SummarizedExperiment | Session information

Introduction | Installation | What this package is for | Quick start | Step 1: Diagnose | Step 2: Estimate | Step 3: Train a texture model once | Step 4: Sensitivity analysis | Step 5: Recommend | Step 6: Prospective analysis | Survival outcomes | Putting it together | Further reading | Session information

Introduction | Installation | What sensitivity analysis does | A minimal example | Inferring confounding from the data | Inferred vs. default confounding | Further reading | Session information

Introduction | Installation | Time-to-event outcomes in ICONIC | Two effect scales | COCA incompatibility | A minimal example | Cox log-HR scale | RMST scale | Diagnosis and eligibility | Further reading | Session information

Introduction | Installation | Two simulation modes | The hybrid GAN + copula architecture | Training the texture model | Attaching the texture model | When to use each mode | Further reading | Session information

Introduction | Background - bppg Theory | Peptides | Bipartite Graphs | Installation of bppg | Asking for help | Citing bppg | Quick start to using bppg | Data | Example Data | Import | FASTA file | Build Quantification Graph | Optimize Relative Quantification | Combine and Export | Parallelization | Further information | Comparison with other related packages | R session information | Bibliography

Introduction | Installation and loading | Test dataset | Preparing inputs for ECLIPSE | Contig files | Option 1: use folder paths | Option 2: use contig file paths | Seurat object meta.data | Column to pass to original_barcode | Column to pass to batch | Column to pass to donor | Column to pass to group | Running the ECLIPSE function | Description of function | Running ECLIPSE | Running tcrDoubletDetect | Downstream analysis with r Biocpkg("scRepertoire") | Conclusions | Supplemental information | Helper function: findThreeChainClones | ECLIPSE additional arguments | Seurat object columns description | Session info

Overview | Example data | Building a gene annotation file | Downstream: gene-level analysis with MAGMA | Sampling down (SampledDownAnnotation()) | Session information

Introduction | Installation | Available resources | Discover resources | Retrieve a selected resource | Identifiers, coordinates, and sequence information | Derive transcription start sites | TxDb structural annotation | Provenance and citation | Session information

Introduction | Installation | Available datasets | Discovering the data on ExperimentHub | Loading a dataset | Exploring the data | Data provenance | Citing the data | Session info

Overview | Load the data | Select a cell population | Aggregate into pseudobulk samples | Differential expression | Score candidate regulators | Filtering hits that cannot be human regulators | Visualise the results | Cell-level scoring with singscore | Session information

Overview | Load the GLUCOLD data | Check and align the samples | Define the visit order | Create the LongOmicsTraj object | Empirical trajectory estimation | maSigPro trajectory estimation | OTS distribution | Query treatment-specific trajectories | Query result summary | Representative genes | Prepare expression profiles | Add OTS labels | Plot representative trajectories | Using your own data | Summary | Quick reference | Session information

Overview | Required packages | Load and prepare the GLUCOLD example | Construct feature-level trajectories | Map feature trajectories to OTS labels | Cluster genes across all treatment groups | Map cluster centres to topology labels | Inspect topology composition within clusters | Trajectory-arm plot | Quantify topology purity | Calculate topology coverage and duplication | Plot cluster-centre trajectories | Alternative clustering with PAM | Topology-aware FlexMix clustering within one treatment arm | Plot the cluster-selection path | Plot representative cluster trajectories and OTS composition | Plot topology deviations | Plot representative genes | Interpretation | Session information

Introduction | How this fits in a Bioconductor workflow | Workflow at a Glance | Installation | Load Packages | Input Data Contract | Standard Bioconductor Accessors | Minimal MAE Workflow Example | Parameter Reference (Conbin, Multiclass, and Survival) | Common Wrapper Parameters (IntegratedLearner) | Conbin-Specific Parameters (Continuous/Binary Path) | Multiclass-Specific Parameters (Native Multiclass Path) | Survival-Specific Parameters (via ...) | Supported Models and Fusion Modules | Supported Models | Supported Fusion Outputs | Output Reference: What You Get and How to Access It | Conbin Outputs (Binary/Continuous) | Multiclass Outputs | Survival Outputs (Single/Early/Late) | Importance Outputs (Conbin, Multiclass, and Survival) | Quick Access Snippets | Example 1: Binary Outcome (IBD Classification) | Step 1: Load and Inspect Training and Validation Data | Step 2: Inspect Layer Assays | Step 3: Fit the Model | Step 4: Inspect and Interpret Outputs | Example 2: Continuous Outcome (Gestational Age) | Step 1: Load and Inspect Data | Step 2: Fit Continuous Model | Step 3: Evaluate Predictive Accuracy | Step 4: Uncertainty and Feature-Level Interpretation (BART) | Example 3: Multiclass Outcome (Franzosa MAE with External Validation) | Example 4: Survival Outcome (Time-to-event) | Interpret Survival Outputs | Session Information | References | Citation

Introduction | Quick Start Example | Session information

Scope | Build a relationship table | Map relationships to genomic features | Annotate regions | Summarise without scoring drugs | Session information

Installation | Install from Bioconductor | Install the development version from GitHub | Introduction | Finding available species | Downloading the databases | Loading a TxDb (Arabidopsis example) | Available columns and keys | Retrieve all genes | Retrieve all transcripts | Retrieve all exons | Filter by gene ID | Retrieving transcript types | Working with chromosome names | Soybean (Glycine max) example | Rice (Oryza sativa) example | Contributing new species | Data sources and licensing | Session information

The demonstration data | Quality control | Relationships from the pedigree | Relationships from the markers | Combining both: the single-step matrix | Session information

Introduction | Vignette outline | How the Shiny app is organised | Installation | A — Bioconductor (when the package is submitted and available) | B — GitHub (when not yet on Bioconductor) | C — Local clone (developers) | Run latest code for analysis | Launch the Shiny application | App steps at a glance | Evaluated package checks | Step-by-step Shiny walkthrough | Home — choose a workflow | Group B — Automated Prediction (start here) | Group C — Gene Panel Prediction | Group A — Clinical Prediction | Explorer and Audit (after a run) | VCF annotation requirements | Sample inputs | Troubleshooting | Session Information

Introduction | Getting started | Installation | Reading data | Core plots | Manhattan plot | Broken y-axis for extreme p-values | Significance thresholds | Effect-size confidence bound | Palette and theme variations | QQ plot | Miami plot | Locus zoom | Novel visualizations | Genome-wide p-value heatmap | Effect-size volcano | Circular Manhattan | Summary dashboard | Gene annotation and top hits | Gene labels on peaks | Top hits table | Region highlights | Genomic tracks | Non-human organisms | Multi-study comparisons | Multi-trait Manhattan | Enrichment Manhattan | Circular Manhattan (multi-ring) | Post-GWAS analysis plots | PheWAS plot | Colocalization locus plot | Fine-mapping credible set | Genetic correlation matrix | Genetic architecture | Trumpet plot: power to detect | Forest plots and cross-study comparison | Genome-wide density | SNP density karyogram | Species support | Density vs signal comparison | Themes, palettes, and presets | Color palettes | Journal themes | Presets | Performance | Multi-panel figures | Saving figures | Session info

Introduction | Example datasets | Bulk Transcriptomics Analysis | Load the dataset and the gene signature | Compute SPAROscore | Combine scores with the sample metadata | Compare treated and untreated samples | Statistical comparison | Visualising pathway activity on a PCA | Relationship between SPAROscore and individual marker genes | Summary | Single-cell RNA-seq analysis | Load the dataset from muscData | Load the gene signature from MSigDB | Comparing stimulated and control cells | Interferon activity across cell types | Visualising pathway activity on the UMAP | Efficiently scoring additional pathways | Pseudo-bulk analyses | Aggregate cells into pseudo-bulk profiles | Compute scores at pseudo-bulk level | Visualise pseudo-bulk level scores | Spatial transcriptomics analysis | Load the DLPFC dataset from SpatialLIBD | Visualise the tissue morphology | Defining a oligodendrocyte gene signature | Visualising pathway activity across the tissue | Pathway activity across anatomical regions | Conclusion

Introduction to QuickBLAST | Setup and Instance Management | Managing Memory | Raw Sequence Comparison | Remote NCBI Searching | File-to-File Comparisons | Local Database Creation and Searching | High-Performance Output with Apache Arrow | Conclusion

Supported input data structures | Example datasets | Load the gene expression dataset | Load the gene signatures | Gene expression as matrix-like data structures | Dense matrix | SparseMatrix | DelayedArrays | Data frame | Verify if the output scores are the same with differing input formats | Gene signature representations | Single signature as a character vector | Single signature as a GeneSet object | Multiple signatures as a named list | Multiple signatures as a GeneSetCollection object | SummarizedExperiment and derived objects | Seurat objects

What is SPAROscore? | How to use SPAROscore? | Load the dataset | Compute signature scores using SPAROscore | Visualise the results | What transcriptomics technologies can SPAROscore handle? | What data structures can SPAROscore handle? | What are some advanced capabilities of SPAROscore?

Introduction | Background | A complete example | Initialization | Clustering parameters | Optimization parameters | Output | Relationship to Harmony | Session information

Overview | 1 Gene-group file and kinship | 2 Binary phenotype (family = binomial()) | 3 Continuous phenotype (family = gaussian()) | 4 Count phenotype (family = poisson()) | 5 Adjusting for covariates | 6 Where to get gene-group files for other species/builds | 7 Other ways to adapt this to your own data

Introduction | Installation | A complete in-memory workflow | Reading FASTQ and public metadata | References | Session information

Introduction | Many ways of compiling coverage | Example heatmaps created using different bigwig generation procedures | Working with fragment files as an input | Session information

Plotting signals in a region | Merging signal from different tracks | Using an EnsDb object | Further track customization | Session info

Introduction | Usage | scmageck_rra | scmageck_lr | Output | Contact us | Session info

Overview | Content | Dose Response Plots | Single-agent Data | Combination Data | Metrics plot | Co-dilution Data | Quality Control | Merging of Manifest, Treatment and Raw data using gDRimport | Mapping Controls to Treated | Degree of Errors in Normalization Values | Averaged Values | Accuracy of Fitting Dose-response Curves | Quality Control - Fitting Precision | Correlation between PRISM and DepMap | Volcano plot | Scatter plot with correlation | Boxplots | SessionInfo

Connect to ExperimentHub | Find PTMsToPathwaysData records | Download a resource | Inspect downloaded objects | Session info

Environment setup | 1. Load and Prepare CRC Dataset | 2. Differential Expression Analysis | 3. Label-Switch Classification | 4. UMAP Visualization of Feature Subsets | 5. Quadrant Assignment and Delta–Fold-Change Scatter Plot | 6. Expression Patterns of Representative Features | 7. Heatmap of Quadrant-Defined Features | 8. Pathway Enrichment Analysis (Reactome) | Session information

Introduction | Function Overview | Parameters | Return Value | Usage Examples | How It Works | The Measure of Concordance (MoC) Score | Dynamic Programming for Optimal TAD Selection | Important Notes

Overview | Shipped datasets | ExperimentHub datasets | Data provenance | Session info

Generating tables of values from a qseaSet | Quality Control | Relative Enrichment | Fragments without pattern | Hyper-stably methylated regions | Summary | Session info

Differentially Methylated Regions | Introduction | Output | Covariates | Additional Options | Annotation | Other Genomes | Analysing DMRs | Summarising by Gene | Plotting Windows | Session info

PCA functions | Basic use | Editing the sampleTable | PlotPCA function arguments | Anotating samples by colour and/or shape | Colour and shape palettes | NA values | Components | Point size | Text annotations | Output of getPCA() | getPCA() function arguments | Setting number of top variable windows to use | Samples used to calculate variability | Inputting a pre-calculated dataTable | Normalisation Method | Filtering regions | UMAPs | Session info

Introduction | Sample Metadata | Making a qseaSet | Selecting the windows | Coverage-related options | Note on Proper Pairs | Copy Number Variation | Calculating CpG Density | Offset Reads | Beta Calculation Fitting | Note on TMM Normalisation | Session info

Introduction | Example Dataset | Parts of a qseaSet | Sample Table | Counts Matrix | Windows | Library Information | Copy Number Variation | Parameters, Enrichment, Zygosity | Parallelisation | Session info

Simulate a small AP-MS experiment | Validate the input tables | Spectral-count scoring | Volcano plot | Intensity scoring | See also | Session information

Introduction | Installation | Quick start | Loading the package and example data | The basic overlay model | Choosing the foreground and background | Plotting several genes | Splitting plots by groups | Shared scales and legends | Nested group plots | Palettes | Categorical palettes and Named categorical palettes | Rasterizing dense point layers | Saving plots | Session information

Goal | 1. Single-gene query — query_gene() | 2. Multi-gene comparison — query_panel() | 3. Multi-parameter candidate listing — list_candidates() | 4. Filter audit trail — trace_filters() | 5. Plain-text summary — summarize_gene_evidence() | Atlas metadata | Next | Session

Introduction | Installation | A first plot | Categorical colour mapping | Continuous colour mapping | Working with Bioconductor data structures | Linking plots in Shiny | Network requirements | Session info

What works at what scale | How the wire format works | A benchmark you can run yourself | Sizing inside the host viewport | Memory levers for very large data | Comparison with other R packages | Where the next jump comes from | Session info

Overview | Installation | Load package | Available datasets | MESOMICS datasets | lungNENomics datasets | Test datasets | Data access | Related package

Introduction | Installation | Basic setup | Lookups | Traversals of GO | Ancestor (generalization) retrieval | Descendant | Cross-ontology activities | Quick overview of RDF and ontological entailments | Common predicates | Some lookups in Relational Ontology | Some lookups for Basic Formal Ontology | Decoding external predicate CURIEs | Learning about an ontology | Statements related to a selected CURIE | Migrating from GO.db | AnnotationDbi "select" | Environments | Additional examples | Term to descendant ids | Term to human genes

Introduction | Rich Gene Queries | Get ALL genes with FULL attributes | Filter by gene_type | Filter by annotation confidence level | Rich Transcript Queries | Exon Queries with Context | CDS and Protein Information | UTRs and Codons | Transcripts by Gene | Region Queries | Performance Tips | Session information

Introduction | Toy datasets in MOTL | Main steps | Load libraries | Learning dataset Lrn | Learning dataset metadata | Learning dataset factorization model | Learning dataset initialization | Target dataset Trg | Transfer learning inputs | Transfer learning using MOTL | Session info | References

Getting read counts in regions of interest | Quality control | Coverage statistics | Fragment length distributions | TSS enrichment | Peak calling | Region merging | Region overlapping | Session information

Introduction | Reading signal in/around a set of regions | Extracting and manipulating signal matrices | Normalization | Plotting heatmaps | Color-scale trimming | Different colorscales for different tracks | Scaled regions | Heatmap rasterization | Sorting and clustering | Plotting aggregated signals | Visualizing DNAme and sparse signals | Session information

Introduction | Applying normalization factors when generating the bigwig files | Obtaining normalization factors for a set of signal files | Normalization methods | Obtaining normalization factors from the signal matrices themselves | Session information

Introduction | Parameters for bHIVE() | How bHIVE Works | Example of Parameter Selection | Applications | Clustering | Classification | Hyperparameter Tuning | Using the caret wrapper | Applying the caret-based model | honeycombHIVE: Multilayered bHIVES | Gradient-based Refinement | Applying Refinement to honeycombHIVE | Visualizing Results with visualizeHIVE | Example 1: Scatterplot for Clustering Results | Example 2: Violin Plot for Classification Results | Conclusions

Introduction | Before you run all chunks | Main package features | 1. Basic operations | 1.1 Project initialization | 1.2 Writing and reading tables | 1.3 Advanced analysis with SQL queries (ol_query) | JOINs and subqueries | Lazy evaluation and integration with dplyr | 1.4 Advanced analysis with aggregation and window functions | Calculating gene expression statistics | Ranking genes | Moving average and cumulative sum | Combining with lazy evaluation | 1.5 Saving and loading R objects | 2. Tracking dependencies | 2.1 Saving data with specified dependencies | 2.2 Checking dependencies | 2.3 Importing and exporting Parquet files | Parquet export | Parquet import | Performance tips | 3. Version management | 3.1 Tagging and labeling | 3.2 Creating multiple versions | 3.3 Listing and comparing versions | 3.4 Loading a specific version | 4. Commits and history management | 4.1 Creating commits | 4.2 Viewing history | 5. Visualizing dependencies | 5.1 Creating dependency graphs | 6. Restoring a project | 6.1 Restoring state using labels | 7. Advanced features | 7.1 Filtered data loading | 7.2 Loading with lazy evaluation | 7.3 Deleting tables | 8. Bioconductor integration | 8.1 Creating a SummarizedExperiment | 8.2 Creating a MultiAssayExperiment | 9. Practical workflow example | 9.1 Complete differential expression analysis workflow | 10. Conclusion | List of key functions | Project management | Data saving and loading | Version management | History management | Dependencies | Listing | Bioconductor | Others | Best practices | Session Information

Introduction | Before you run all chunks | Covered layers | Initialization | 1. Bulk RNA-seq (SummarizedExperiment) | 2. Single-cell RNA-seq (SingleCellExperiment) | 3. Multi-omics cohort (MultiAssayExperiment) | 4. Proteomics / Metabolomics raw MS (Spectra) | 5. Proteomics quantification graph (QFeatures) | 6. Integrated LC-MS container (MsExperiment) | 7. Cross-layer release manifest | 8. Validation with Tutorial-Derived Data | 8.1 Official MultiAssayExperiment data (miniACC) | 8.2 Official Spectra data (fft_spectrum) | 9. Automated Effectiveness Checks | Summary | Session Information

Introduction | Before you run all chunks | Project setup | Preparing the data | Phase 1: Filtering data with SQL queries | Phase 2: Statistical analysis with aggregation functions | Calculate statistics for each gene | Extract top expressed genes | Add rankings | Compute cumulative statistics | Phase 3: Sharing results with Parquet | Exporting results | Importing external data | Phase 4: Compare multiple versions with database views | Scenario: Compare two different analysis methods | Create a view: Compare methods | Using the view: Check concordance | Create multiple views | List views | Deleting views | Integrated workflow example: Combine all features | Practical tips and best practices | Use cases for Phase 1 (SQL queries) | Use cases for Phase 2 (aggregation functions) | Use cases for Phase 3 (Parquet) | Use cases for Phase 4 (views) | Combination patterns | Summary | Session Information

Introduction | Before you run all chunks | Recommended Next Guides | Key Features | Installation | Basic Usage | Initializing a Lake | Storing and Reading Data | Filtering with Formula Syntax | dplyr Integration (Automatic Lineage Tracking) | Joining Multiple Tables | QueryBuilder | Version Control | Snapshots and Tags | Viewing History | Lineage (Data Provenance) | Checking Dependencies | Lineage Visualization | Lightweight Mode (Integrating with Existing Code) | observe: Track without Code Changes | wrap: Wrap Functions for Tracking | Defining Pipelines | Custom Operators | Bracket Notation | Import/Export | Bioconductor Integration | Global Shortcuts | Migration from Legacy API | Summary | Session Information

はじめに | 全コードを実行する前に | 次に読むガイド | 主な特徴 | インストール | 基本的な使い方 | Lakeの初期化 | データの保存と読み込み | Formula構文でフィルタリング | dplyr統合（自動リネージ追跡） | 複数テーブルのJOIN | QueryBuilder | バージョン管理 | スナップショットとタグ | 履歴の確認 | リネージ（データ系譜） | 依存関係の確認 | リネージの可視化 | 軽量モード（既存コードへの導入） | observe: コード変更なしで追跡 | wrap: 関数をラップして追跡 | パイプラインの定義 | カスタム演算子 | ブラケット記法 | インポート/エクスポート | Bioconductor統合 | グローバルショートカット | 旧APIからの移行 | まとめ | Session Information

はじめに | 全コードを実行する前に | 対象レイヤー | 初期化 | 1. Bulk RNA-seq (SummarizedExperiment) | 2. Single-cell RNA-seq (SingleCellExperiment) | 3. Multi-omics cohort (MultiAssayExperiment) | 4. Proteomics / Metabolomics raw MS (Spectra) | 5. Proteomics quantification graph (QFeatures) | 6. LC-MS統合コンテナ (MsExperiment) | 7. クロスレイヤーの公開マニフェスト | 8. チュートリアル由来データでの検証 | 8.1 MultiAssayExperiment公式データ (miniACC) | 8.2 Spectra公式データ (fft_spectrum) | 9. 有効性チェック（自動） | まとめ | Session Information

はじめに | 全コードを実行する前に | プロジェクトのセットアップ | データの準備 | Phase 1: SQLクエリによるデータフィルタリング | Phase 2: 集計関数による統計解析 | 遺伝子ごとの統計量を計算 | 発現量上位の遺伝子を抽出 | ランキングを追加 | 累積統計の計算 | Phase 3: Parquetでの結果共有 | 結果のエクスポート | 外部データのインポート | Phase 4: データベースビューで複数バージョンを比較 | シナリオ: 2つの異なる解析手法を比較 | ビューの作成: 手法間の比較 | ビューの活用: 一致度の確認 | 複数のビューを作成 | ビューの一覧表示 | ビューの削除 | 統合ワークフロー例: 全機能を組み合わせる | 実践的なヒントとベストプラクティス | Phase 1 (SQLクエリ) の活用場面 | Phase 2 (集計関数) の活用場面 | Phase 3 (Parquet) の活用場面 | Phase 4 (ビュー) の活用場面 | 組み合わせのパターン | まとめ | Session Information

はじめに | 全コードを実行する前に | パッケージの主な機能 | 1. 基本操作 | 1.1 プロジェクトの初期化 | 1.2 テーブルの書き込みと読み込み | 1.3 SQLクエリによる高度な分析 (ol_query) | JOINとサブクエリ | 遅延評価とdplyrとの統合 | 1.4 集計・ウィンドウ関数による高度な分析 | 遺伝子発現統計の計算 | 遺伝子のランキング | 移動平均と累積和 | 遅延評価との組み合わせ | 1.5 Rオブジェクトの保存と読み込み | 2. 依存関係の追跡 | 2.1 依存関係を指定してデータを保存 | 2.2 依存関係の確認 | 2.3 Parquet ファイルのインポート・エクスポート | Parquetエクスポート | Parquetインポート | パフォーマンスのヒント | 3. バージョン管理 | 3.1 タグ付けとラベル付け | 3.2 複数バージョンの作成 | 3.3 バージョン一覧と比較 | 3.4 特定バージョンの読み込み | 4. コミットと履歴管理 | 4.1 コミットの作成 | 4.2 履歴の確認 | 5. 依存関係の可視化 | 5.1 依存関係グラフの作成 | 6. プロジェクトの復元 | 6.1 ラベルを使った状態の復元 | 7. 高度な機能 | 7.1 データのフィルタリング読み込み | 7.2 遅延評価での読み込み | 7.3 テーブルの削除 | 8. Bioconductor統合 | 8.1 SummarizedExperimentの作成 | 8.2 MultiAssayExperimentの作成 | 9. 実践的なワークフロー例 | 9.1 完全な差次発現解析ワークフロー | 10. まとめ | 主要な関数一覧 | プロジェクト管理 | データ保存・読み込み | バージョン管理 | 履歴管理 | 依存関係 | 一覧表示 | Bioconductor | その他 | ベストプラクティス | Session Information

Introduction | Installation | Detecting recombination | Detecting putative recombination intervals | Filtering for double recombination | Visualizing GenomicRanges output from xoDetect() using trackViewer and rtracklayer | Runs of homozygosity | Phasing at Informative SNPs | Session Information

Additional file types | Additional matrix types | Importing into other projects

Usage | Example code with R | Read genotype data into R | Use R to run analysis at C++ level | Example code with C++17 | Data types | Session info

Overview of the analysis | Installation | Interactive analysis using the DEP Shiny apps | Differential analysis | Example dataset: Ubiquitin interactors | Loading of the data | Data preparation | Generate a SummarizedExperiment object | Prerequisites of the SummarizedExperiment object | Filter on missing values | Normalization | Impute data for missing values | Differential enrichment analysis | Visualization of the results | PCA plot | Correlation matrix | Heatmap of all significant proteins | Volcano plots of specific contrasts | Barplots of a protein of interest | Frequency plot of significant proteins and overlap of conditions | Results table | Generate a data.frame from the resulting SummarizedExperiment object | Save your results object for reuse | Workflow functions for the entire analysis | LFQ-based DEP analysis | TMT-based DEP analysis | Session information

Overview

Overview

Goal of this vignette | Simulate data | Generate intensity values | Introduce missing values | Generate a SummarizedExperiment | Filter proteins based on missing values | Visualize the extend of missing values | Filter options | Scaling and variance stabilization | Data imputation of missing data | Explore the pattern of missing values | Imputation options | Advanced imputation methods | Mixed imputation on proteins (rows) | Mixed imputation on samples (columns) | Test for differential expression | Number of identified differentially expressed proteins | ROC curves | Differences in response | Session information