Package: ClustIRR 1.10.0

Simo Kitanovski

ClustIRR: Clustering of Immune Receptor Repertoires

ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.

Authors:Simo Kitanovski [aut, cre], Kai Wollek [aut]

ClustIRR_1.10.0.tar.gz
ClustIRR_1.10.0.zip(r-4.7-x86_64)ClustIRR_1.10.0.zip(r-4.6-x86_64)ClustIRR_1.10.0.zip(r-4.5-x86_64)
ClustIRR_1.10.0.tgz(r-4.6-x86_64)ClustIRR_1.10.0.tgz(r-4.6-arm64)ClustIRR_1.10.0.tgz(r-4.5-x86_64)ClustIRR_1.10.0.tgz(r-4.5-arm64)
ClustIRR_1.10.0.tar.gz(r-4.7-arm64)ClustIRR_1.10.0.tar.gz(r-4.7-x86_64)ClustIRR_1.10.0.tar.gz(r-4.6-arm64)ClustIRR_1.10.0.tar.gz(r-4.6-x86_64)
ClustIRR_1.10.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
ClustIRR/json (API)

# Install 'ClustIRR' in R:
install.packages('ClustIRR', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/snaketron/clustirr/issues

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3
Datasets:
  • BLOSUM62 - BLOSUM62 matrix
  • CDR3ab - Datasets 'CDR3ab', 'D1' and 'D2' with TCRalphabeta mock repertoires
  • D1 - Datasets 'CDR3ab', 'D1' and 'D2' with TCRalphabeta mock repertoires
  • D2 - Datasets 'CDR3ab', 'D1' and 'D2' with TCRalphabeta mock repertoires
  • mcpas - CDR3 sequences and their matching epitopes obtained from McPAS-TCR
  • tcr3d - CDR3 sequences and their matching epitopes obtained from TCR3d
  • vdjdb - CDR3 sequences and their matching epitopes obtained from VDJdb

On BioConductor:ClustIRR-1.11.0(bioc 3.24)ClustIRR-1.10.0(bioc 3.23)

clusteringimmunooncologysinglecellsoftwareclassificationbayesianbiomedicalinformaticsmathematicalbiologyb-cell-receptorbioinformaticsimmunoinformaticsimmunologyquantitative-methodsrep-seqrepertoire-analysist-cell-receptoronetbbcpp

6.40 score 5 stars 13 scripts 305 downloads 19 exports 113 dependencies

Last updated from:0634e77846 (on RELEASE_3_23). Checks:12 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksNOTE306
linux-devel-arm64NOTE330
linux-devel-x86_64NOTE408
source / vignettesOK448
linux-release-arm64NOTE341
linux-release-x86_64NOTE470
macos-release-arm64NOTE220
macos-release-x86_64NOTE588
macos-oldrel-arm64NOTE228
macos-oldrel-x86_64NOTE504
windows-devel-x86_64NOTE444
windows-release-x86_64NOTE468
windows-oldrel-x86_64NOTE480
wasm-releaseOK256

Exports:clustirrdcodecode_all_communitiesdecode_communitydetect_communitiesget_ag_gene_hitsget_ag_species_hitsget_beta_cprob_agget_beta_violin_agget_cdr3_motifsget_clustirr_clustget_clustirr_inputsget_community_feature_purityget_community_feature_statsget_cosine_similarityget_honeycombsget_nradsplot_graphsave_interactive_graph

Dependencies:abindaskpassbackportsbase64encBHBiocFileCacheBiocGenericsBiostringsbitbit64blobbslibcachemcallrcheckmateclicpp11crayoncurlDBIdbplyrdescdigestdistributionaldplyrevaluatefarverfastmapfilelockfontawesomefsgenericsggforceggplot2ggseqlogogluegridExtragtablehighrhtmltoolshtmlwidgetshttr2igraphinlineIRangesisobandjquerylibjsonliteknitrlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisemimemsanumDerivopensslotelpillarpkgbuildpkgconfigplyrpolyclipposteriorprocessxpspurrrQuickJSRR6RADanalysisrappdirsrBLASTRColorBrewerRcppRcppEigenRcppParallelreshape2rlangrmarkdownRSQLiterstanrstantoolsS4VectorsS7sassscalesSeqinfosfsmiscStanHeadersstringdiststringistringrsyssystemfontstensorAtibbletidyrtidyselecttinytextweenrutf8vctrsviridisLitevisNetworkwithrxfunXVectoryaml

Decoding T- and B-cell receptor repertoires with ClustIRR
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 | Compare $\beta$s of clonotypes specific for CMV, EBV, flu or MLANA? | Posterior predictive checks | Differential community abundance results $\rightarrow$ par. $\delta$ | Conclusion: you can also use custom community occupancy matrix for DCO!

Last update: 2026-04-10
Started: 2026-01-05

Finding biological condition-specific changes in T- and B-cell receptor repertoires with ClustIRR

Last update: 2026-04-09
Started: 2026-01-05

Readme and manuals

Help Manual

Help pageTopics
BLOSUM62 matrixBLOSUM62
clust_irr classclass:clust_irr clust_irr clust_irr-class get_clustirr_clust get_clustirr_clust,clust_irr-method get_clustirr_inputs get_clustirr_inputs,clust_irr-method
Clustering of immune receptor repertoires (IRRs)clustirr
Datasets 'CDR3ab', 'D1' and 'D2' with TCRalphabeta mock repertoiresCDR3ab D1 D2
Model-based differential community occupancy (DCO) analysisdco
Decode all graph communitiesdecode_all_communities
Decode graph communitiesdecode_community
Graph-based community detection (GCD)detect_communities
Annotate antigen gene hits in node summaryget_ag_gene_hits
Annotate antigen species hits in node summaryget_ag_species_hits
Visualize cumulative probability of beta means for antigen-specific communitiesget_beta_cprob_ag
Visualize distribution of beta means in each repertoire as violin plotsget_beta_violin_ag
Generate CDR3 motif for communitiesget_cdr3_motifs
Compute community purity with respect to a node featureget_community_feature_purity
Compute descriptive statistics of a community node featureget_community_feature_stats
Compute and Visualize Cosine Similarity (CS)get_cosine_similarity
Generate honycomb plot: visualize community occupancy of pairs of immune receptor repertoiresget_honeycombs
Compute Normalized Rank Abundance Distributions (NRADs)get_nrads
CDR3 sequences and their matching epitopes obtained from McPAS-TCRmcpas
Plot ClustIRR graphplot_graph
Save interactive ClustIRR graphsave_interactive_graph
CDR3 sequences and their matching epitopes obtained from TCR3dtcr3d
CDR3 sequences and their matching epitopes obtained from VDJdbvdjdb