A comprehensive analytics framework for building reproducible pipelines on T-cell and B-cell immune receptor repertoire data.
    Delivers multi-modal immune profiling (bulk, single-cell, CITE-seq/AbSeq, spatial, immunogenicity data), 
    feature engineering (ML-ready feature tables and matrices), and biomarker discovery workflows
    (cohort comparisons, longitudinal tracking, repertoire similarity, enrichment). 
    Provides a user-friendly interface to widely used AIRR methods — 
    clonality/diversity, V(D)J usage, similarity, annotation, tracking, and many more.
    Think Scanpy or Seurat, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or 
    VDJ sequencing data. A successor to our previously published "tcR" R package (Nazarov 2015).

Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.

Provides a unified data layer for single-cell, spatial and bulk
 T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject,
 but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or 
 VDJ sequencing data.

Provides a literature-derived database of immune cell markers
    formatted as Gene Matrix Transposed (GMT) files. Users can search immune
    cell signatures, retrieve marker lists, export GMT files, create custom
    marker sets, and score gene-by-cell expression matrices with dependency-free
    rank-based or mean-expression methods. Cell subpopulations are distinguished 
    by their source PMIDs. For the core curation of the lung cell atlas, 
    see Travaglini et al. (2020) <doi:10.1038/s41586-020-2922-4>. For the 
    pan-cancer B cell signatures, see Fitzsimons et al. (2024) 
    <doi:10.1016/j.ccell.2024.09.011>.