scRepertoire Documentation

repository·devel·Indexed 18 days ago

https://github.com/borchlab/screpertoire

An R-based toolkit for single-cell immune profiling that combines RNA quantification with T-cell and B-cell receptor (TCR/BCR) profiling. It supports multiple clonal formats including 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R, and integrates with R-based pipelines like Seurat. The toolkit can be extended with deep-learning tools such as Trex, Ibex, and immApex.

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What's inside scRepertoire

  1. How to use scRepertoire for single-cell immune profiling

    devel

    scRepertoire is a toolkit designed to couple RNA quantification with immune cell receptor profiling (TCR/BCR) at the single-cell level.

    It supports multiple single-cell clonal formats, including:

    • 10x
    • AIRR
    • BD
    • MiXCR
    • TRUST4
    • WAT3R

    The framework is designed to interact with popular R-based single-cell data pipelines (such as Seurat) and provides extensions for deep learning-based analysis.

  2. Deep Learning Extensions for scRepertoire

    devel

    scRepertoire can be extended with deep-learning tools for immune receptor analysis:

    • Trex: For deep-learning-based autoencoding of T cell receptors (TCR).
    • Ibex: For B cell receptor (BCR) analysis.
    • immApex: Used for building custom deep-learning models with immune receptors.
  3. Quick Start: Load, combine, and visualize immune receptor data

    devel

    The standard workflow for using scRepertoire involves three main steps: combining contig data into clones, visualizing clonal frequency, and attaching that data to a single-cell object (like a Seurat object).

    Note: This example assumes contig_list and scRep_example are already available in your environment.

    library(scRepertoire)
    
    # Load and combine contigs into clones
    combined.TCR <- combineTCR(contig_list,
                               samples = c("P17B", "P17L", "P18B", "P18L",
                                           "P19B", "P19L", "P20B", "P20L"))
    
    # Visualize clonal frequency
    clonalQuant(combined.TCR, clone.call = "strict", chain = "both")
    
    # Attach clonal data to a single-cell object
    scRep_example <- combineExpression(combined.TCR, scRep_example)
  4. Install scRepertoire via Bioconductor

    devel

    The current stable version of scRepertoire is available through Bioconductor. Use BiocManager::install() to install it.

    if (!require("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    
    BiocManager::install("scRepertoire")
  5. Install scRepertoire development branch

    devel

    To install the development branch from GitHub, you must also install the required dependency immApex. Use remotes::install_github() to install both packages.

    remotes::install_github(c("BorchLab/immApex", "BorchLab/scRepertoire@devel"))