Introduction | Somatic Hypermutation Engine | Idiotypic Network Regulation | Germinal Center Selection | Microenvironment | V(D)J Gene Library Initialization | Two-Signal Activation Gate | Memory Pool | Class Switcher | Convergent Selection | Putting It All Together | Session Information

Introduction | Installation | A mass cytometry dataset | Clustering a SingleCellExperiment | Reading the prototypes | Plotting the cells with their prototypes | bHIVE through bluster | Comparison with FlowSOM and bluster methods | Label transfer to new cells | The R6 engine and immune modules | Other tools in the package | Session information

Introduction | Loading Libraries | Loading Data | What data to load into scRepertoire? | Demonstrating Manual Data Loading (10x Genomics) | Other alignment workflows | Supported Formats and Expected File Names | Multiplexed Experiment | Important Considerations for createHTOContigList() | Example Data in scRepertoire | Combining Contigs into Clones | combineBCR | How combineBCR() Groups Related Clones | Advanced: Grouping by Alignment | Filtering and Cleaning Data | Retaining Full-Length Sequences | Additional Processing | addVariable: Adding Variables for Plotting | subsetClones: Filter Out Clonal Information | exportClones: Save Clonal Data | clonalBin: Bin Clones by Frequency or Proportion | Basic Usage with Proportion | Using Frequency-Based Binning | Grouping by a Variable | annotateInvariant | Basic Clonal Visualizations | cloneCall: How to call clones. | clonalQuant: Quantifying Unique Clones | clonalAbundance: Distribution of Clones by Size | clonalLength: Distribution of Sequence Lengths | clonalCompare: Clonal Dynamics Between Categorical Variables | clonalScatter: Scatterplot of Two Variables | Visualizing Clonal Dynamics | clonalHomeostasis: Examining Clonal Space | clonalProportion: Examining Space Occupied by Ranks of Clones | Summarizing Repertoires | percentGeneUsage | vizGenes: Flexible Gene Usage Visualization | percentGenes: Quantifying Single Gene Usage | percentVJ: Quantifying V-J Gene Pairings | percentAA: Amino Acid Composition by Residue | positionalEntropy: Entropy across CDR3 Sequences | positionalProperty: Amino Acid Properties across CDR3 Sequence | percentKmer: Motif Quantification | Comparing Clonal Diversity and Overlap | clonalDiversity: Clonal Diversity Quantification | How clonalDiversity() Handles Downsampling and Bootstrapping | Available Diversity Metrics (metric) | clonalRarefaction: Sampling-based Extrapolation | Hill Numbers and Their Interpretation | plot.type Options | Rarefaction using Species Richness (q = 0) | Rarefaction using Shannon Diversity (q = 1) | clonalSizeDistribution: Modeling Clonal Composition | clonalOverlap: Exploring Sequence Overlap | method Parameters for clonalOverlap() | Combining Clones and Single-Cell Objects | Preprocessed Single-Cell Object | Note on Dimensional Reduction | Functions to Exclude VDJ Genes | combineExpression | Calculating cloneSize | Combining both TCR and BCR | Visualizations for Single-Cell Objects | clonalOverlay | clonalNetwork | Filtering Options for clonalNetwork() | highlightClones | clonalOccupy | alluvialClones | Basic Alluvial Plot | Filtering to Top Clones | Highlighting Specific Clones | Visual Customization | getCirclize and vizCirclize | Quick Visualization with vizCirclize | Manual Control with getCirclize | Multi-Level Hierarchical Grouping | Directional Flow | Different Similarity Methods | Subsetting and Proportion | Quantifying Clonal Bias | StartracDiversity | Indices Output from StartracDiversity() | Calculating a Single Index | Pairwise Migration Analysis | clonalBias | Clustering by Edit Distance | clonalCluster: Cluster by Sequence Similarity | Core Concepts | Understanding the threshold Parameter | Demonstrating Basic Use | Demonstrating Clustering with a Single-Cell Object | Returning an igraph Object: | Returning a Sparse Adjacency Matrix | Using Both Chains | Using Different Clustering Algorithms | Conclusion | Session Info

Introduction | Installation | Loading Package | Integration with the scRepertoire Ecosystem | Quick Start | Loading the Example Data | Input Data Format | Working with Single-Cell Data | The runGLIPH() Function | Key Arguments | Method Presets | Running GLIPH2 (Default) | Running GLIPH1 | Understanding the Output | Cluster Properties | Cluster Membership | Motif Enrichment | Network Edges | Customizing the Analysis | Using method = "custom" | Adjusting Significance Thresholds | Choosing a Reference Database | Using a Custom Reference Database | Motif Discovery with findMotifs() | Example | Cluster Scoring with clusterScoring() | Scoring Components | De Novo TCR Generation with deNovoTCRs() | Network Visualization with plotNetwork() | Loading Saved Results with loadGLIPH() | Saving Results to Disk | Performance | Accelerated Computation with immApex | Tips and Best Practices | Session Info

Introduction | Setup | Comparing Clustering Methods | Custom Embedding Workflows | Using Different ESM-2 Model Sizes | Embedding Both Chains | Integration with scRepertoire Clonotypes | Analyzing Selection Pressure | Combining Distance-Based Methods | Working with Large Datasets | HLA Association Analysis | Exporting Results | Best Practices | Session Info

Introduction | Installation | Python Dependencies | Quick Start | Loading the Package and Example Data | Extracting TCR Data | Summarizing TCR Repertoire | Analysis Functions | TCR Clustering with clusTCR | TCR Distance Calculations with tcrdist3 | Generation Probability with OLGA | Protein Language Model Embeddings | Metaclone Discovery with Metaclonotypist | Selection Inference with soNNia | Workflow Example | Session Info

Introduction | Loading Libraries | Acquiring and Preparing Repertoire Data | getIMGT | formatGenes | inferCDR | Generating and Augmenting Sequence Sets | generateSequences | mutateSequences | Feature Engineering from Repertoires | calculateFrequency | calculateEntropy | calculateProperty | calculateGeneUsage | calculateMotif | probabilityMatrix | adjacencyMatrix | buildNetwork | Basic Usage | Standard BCR/TCR Network | Finding Clonotypes with V Gene Filtering | Encoding Sequences for Model Input | sequenceEncoder | onehotEncoder | propertyEncoder | geometricEncoder | tokenizeSequences | sequenceDecoder | Training a Model | Example 1: Classifying Sequences with Random Forest | Example 2: Unsupervised Clustering with PCA and Geometric Encoding | Example 3: Identifying Sequence Communities with Network Analysis | Conclusion | Session Info

Introduction | Installation | Downloading Reference Sequences | Downloading HLA Sequences (IPD-IMGT/HLA) | Downloading TCR/BCR Sequences (IMGT) | Downloading Germline Sets from OGRDB (AIRR) | Working with the Cache | Listing Cached Files | Loading from Cache | Refreshing the Cache | Exporting Reference Databases for External Tools | Export to MiXCR | Export to TRUST4 | Export to Cell Ranger VDJ | Export to IgBLAST | Conclusion | Session Info

Overview | Installation | Loading Processed Single-Cell Data | Getting Gene Sets | Option 1: Built-In gene sets | Option 2: MSigDB via getGeneSets() | Option 3: Define personal gene sets | Option 4: Using msigdbr | Performing Enrichment Calculation | ssGSEA | GSVA | AUCell | UCell | escape.matrix | runEscape | performNormalization | Visualizations | heatmapEnrichment | geyserEnrichment | ridgeEnrichment | splitEnrichment | gseaEnrichment | densityEnrichment | scatterEnrichment | Statistical Analysis | Principal Component Analysis (PCA) | Precomputed Rank Lists | Why do this? | Example enrichIt() workflow | Visualising the enrichment table | Differential Enrichment | Conclusions

Introduction | Installation | Load Libraries | The Data Set | Loading the processed data | Getting Expanded Sequences | Function Parameters | Available Models | Choosing Between CNN and VAE | Choosing Encoding Methods | Running Ibex | Ibex_matrix Function | Parameters | runIbex | Using Ibex Vectors | Comparing the outcome to just one modality | CoNGA Reduction | Conclusion | Session Info

Introduction | The Cache Directory | Finding the Cache | Changing the Cache Location | Offline Workflow | Cache Metadata