scRepertoire: A toolkit for single-cell immune profiling

May 12, 2026 ยท View on GitHub

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Introduction

Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine RNA and immune profiling, the scRepertoire framework supports 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell clonal formats and interaction with popular R-based single-cell data pipelines.

Working with scRepertoire

scRepertoire has a comprehensive website for detailed tutorials and function information.

Quick Start

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)

Installation

Development Branch

immApex is a required dependency. If not using Bioconductor, install both:

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

Bioconductor

The current stable version is available on Bioconductor:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("scRepertoire")

Legacy Version 1

devtools::install_github("BorchLab/scRepertoire@v1")

Getting Data

GitHub limits the size of individual files. To access the full Seurat object paired with scRepertoire, download the .rda from Zenodo. A smaller version of the cohort is built into scRepertoire as scRep_example.

Deep Learning Extensions

scRepertoire is compatible with Trex for deep-learning-based autoencoding of T cell receptors and Ibex for B cell receptors. For building custom deep-learning models with immune receptors, see immApex.

Citation

  • Version 2: Yang, Q, & Safina, K., Nguyen, K., Tuong, Z.K., & Borcherding, N. (2025). "scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling." PLoS Computational Biology https://doi.org/10.1371/journal.pcbi.1012760
  • Version 1: Borcherding, Nicholas, Nicholas L. Bormann, and Gloria Kraus. "scRepertoire: An R-based toolkit for single-cell immune receptor analysis." F1000Research https://doi.org/10.12688/f1000research.22139.2

If you are building your own tool based on scRepertoire, reach out, we are happy to help and make things compatible.

Bug Reports / Feature Requests