Ibex

Methods for BCR single-cell embedding

Bioconductor version: 3.23 · Package version: 1.2.0

Implementation of the Ibex algorithm for single-cell embedding based on BCR sequences. The package includes a standalone function to encode BCR sequence information by amino acid properties or sequence order using tensorflow-based autoencoder. In addition, the package interacts with SingleCellExperiment or Seurat data objects.

Installation

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

BiocManager::install("Ibex")

Details

Maintainer	Nick Borcherding <ncborch@gmail.com>
Author	Nick Borcherding [aut, cre, cph], Qile Yang [ctb] (ORCID: <https://orcid.org/0009-0005-0148-2499>)
License	MIT + file LICENSE
URL	https://github.com/BorchLab/Ibex/
Bug Reports	https://github.com/BorchLab/Ibex/issues
System Requirements	Python (via basilisk)
Downloads rank	108
Source branch	RELEASE_3_23
biocViews	Annotation, Classification, ImmunoOncology, Sequencing, SingleCell, Software

Documentation

A tour of Ibex.

Download

Source package

Dependencies

Depends: R (>= 4.5.0)

Imports: basilisk, immApex (>= 1.3.2), methods, Matrix, reticulate (>= 1.43.0), SeuratObject, scRepertoire, SingleCellExperiment, stats, SummarizedExperiment, tensorflow, tools

Suggests: basilisk.utils, BiocStyle, bluster, dplyr, ggplot2, kableExtra, knitr, lifecycle, markdown, mumosa, patchwork, Peptides, rmarkdown, scater, spelling, testthat (>= 3.0.0), utils, viridis