Moose: Motif organization and optimization search engine for immune receptor repertoires in R

Moose: Motif-aware analysis of adaptive immune receptor repertoires in R Moose is an R package for advanced analysis of adaptive immune receptor repertoires (TCR/BCR), designed to move beyond simple clonal counts toward statistically robust, motif-centric insights. It integrates smoothly with single-cell objects from Seurat and SingleCellExperiment as well as tidy data frames, and is built to complement scRepertoire. More information can be found a BorchLab/MooseWhat’s included in this Zenodo record Moose_example.rda — a compact example object (Seurat/SCE) for tutorials and tests. Moose_MetaData.csv — cohort-level metadata for the example. create_example_data.R — script illustrating how the example was created (h5ad → Seurat/SCE, add TCRs, subset). MooseData_Full.rds — full Seurat object Provenance The example data and recipe are derived from Stephenson et al., Nature Medicine, 2021 (PMID: 33879890, DOI: 10.1038/s41591-021-01329-2). Please read and cite the original work. @article{Stephenson2021NatMed, title = {Single-cell multi-omics analysis of the immune response in COVID-19}, author = {Stephenson, Emily and Reynolds, Gary and Botting, Rachel A. and others}, journal = {Nature Medicine}, year = {2021}, volume = {27}, number = {5}, pages = {904--916}, doi = {10.1038/s41591-021-01329-2}, pmid = {33879890} } Recreate the example (high-level steps) Load the .h5ad into a SingleCellExperiment with zellkonverter; split RNA/ADT from the X layer. Build a Seurat object with RNA counts and metadata; optionally add ADT and dimensional reductions if present. Import TCR contigs, pair chains with scRepertoire (combineTCR()), and add to the single-cell object (combineExpression()). Filter to T-cell subsets with available clonal data; subset to a small cohort; drop ADT for compactness. Save Moose_example.rda (and optionally the larger .rds) and export a tidy Moose_MetaData.csv.