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    },
    {
      "page": "get_beta_violin_ag",
      "title": "Visualize distribution of beta means in each repertoire as violin plots",
      "topics": [
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    },
    {
      "page": "get_cdr3_motifs",
      "title": "Generate CDR3 motif for communities",
      "topics": [
        "get_cdr3_motifs"
      ]
    },
    {
      "page": "get_community_feature_purity",
      "title": "Compute community purity with respect to a node feature",
      "topics": [
        "get_community_feature_purity"
      ]
    },
    {
      "page": "get_community_feature_stats",
      "title": "Compute descriptive statistics of a community node feature",
      "topics": [
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    },
    {
      "page": "get_cosine_similarity",
      "title": "Compute and Visualize Cosine Similarity (CS)",
      "topics": [
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    },
    {
      "page": "get_honeycombs",
      "title": "Generate honycomb plot: visualize community occupancy of pairs of immune receptor repertoires",
      "topics": [
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    },
    {
      "page": "get_nrads",
      "title": "Compute Normalized Rank Abundance Distributions (NRADs)",
      "topics": [
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    },
    {
      "page": "mcpas",
      "title": "CDR3 sequences and their matching epitopes obtained from McPAS-TCR",
      "topics": [
        "mcpas"
      ]
    },
    {
      "page": "merge_clustirrs",
      "title": "Merge immune receptor repertoire similarity graphs",
      "topics": [
        "merge_clustirrs"
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    },
    {
      "page": "plot_graph",
      "title": "Plot ClustIRR graph",
      "topics": [
        "plot_graph"
      ]
    },
    {
      "page": "save_interactive_graph",
      "title": "Save interactive ClustIRR graph",
      "topics": [
        "save_interactive_graph"
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    },
    {
      "page": "tcr3d",
      "title": "CDR3 sequences and their matching epitopes obtained from TCR3d",
      "topics": [
        "tcr3d"
      ]
    },
    {
      "page": "vdjdb",
      "title": "CDR3 sequences and their matching epitopes obtained from VDJdb",
      "topics": [
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      "source": "User_manual_case_study.Rmd",
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      "title": "Decoding antigen-stimulated T cell receptor repertoires with ClustIRR",
      "author": "Simo Kitanovski",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Analysis of T cell receptor (TCR) repertoires",
        "Install and load the current version of ClustIRR",
        "Familiarize with TCR data",
        "Familiarize with gene expression data",
        "Using ClustIRR to compare the two repertoires, CMV vs. Flu",
        "Step 1 Building a joint graph",
        "Using igraph functions to inspect $J$",
        "Step 2 Detecting communities in $J$",
        "Step 3 Finding differentially abundant communities (DACs)",
        "Posterior predictive checks",
        "Taking a peek at the relative community abundances ($\\beta$)",
        "Inspecting DACs of interest",
        "Show the CDR3$\\alpha$\\beta$ sequence logos of community 9",
        "Visualize the graph of community 9",
        "Integration of TCR and GEX data"
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      "created": "2026-07-01 17:04:56",
      "modified": "2026-10-18 12:37:53",
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      "filename": "User_manual_introduction.html",
      "title": "Decoding T- and B-cell receptor repertoires with ClustIRR",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
        "System requirements",
        "ClustIRR algorithm",
        "Input",
        "Algorithm",
        "Step 1. Compute TCR clonotype similarities in a repertoire with cluster_irr",
        "Step 2-3. Construct TCR repertoire graphs and join them into $J$",
        "Run steps 1-3 with clustirr",
        "Inspect the content of clust_irrs",
        "Inspect graphs with plot_graph",
        "You can evaluate $J$ with igraph",
        "Step 4. community detection with detect_communities",
        "Inspecting the outputs of detect_communities",
        "Qualitative similarity between community abundance vectors with get_honeycombs",
        "Quantitative similarity between community abundance vectors with get_cosine_similarity",
        "Summary of communities is provided in the detect_communities outputs",
        "Special functions: decoding communities with decode_community",
        "Step 5. differential community occupancy (DCO) with dco",
        "Step 6. Inspect results",
        "Visualizing the distribution of $\\beta$ with get_beta_violin_ag",
        "Posterior predictive checks",
        "Differential community abundance results $\\rightarrow$ par. $\\delta$",
        "Conclusion: you can also use custom community occupancy matrix for DCO!"
      ],
      "created": "2026-01-05 16:13:08",
      "modified": "2026-10-18 11:18:37",
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      "title": "Finding biological condition-specific changes in T- and B-cell receptor repertoires with ClustIRR",
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      "headings": [],
      "created": "2026-01-05 16:13:08",
      "modified": "2026-04-09 11:36:01",
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