Package: ClustIRR 1.11.10

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

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!