APL
Association Plots
Bioconductor version: 3.23 · Package version: 1.16.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.
DOI: 10.18129/B9.bioc.APL
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("APL")Details
| Maintainer | Clemens Kohl <kohl.clemens@gmail.com> |
| Author | Clemens Kohl [cre, aut], Elzbieta Gralinska [aut], Martin Vingron [aut] |
| License | GPL (>= 3) |
| URL | https://vingronlab.github.io/APL/ |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | DimensionReduction, GeneExpression, RNASeq, Sequencing, SingleCell, Software, StatisticalMethod |
| Package Short Url | https://bioconductor.org/packages/APL/ |
Citation
From within R, enter citation("APL"):
Clemens Kohl, Elzbieta Gralinska, Martin Vingron. APL: Association Plots. doi:10.18129/B9.bioc.APL, R package version 1.16.0, https://bioconductor.org/packages/APL.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | APL_1.16.0.tar.gz |
| Windows binary (x86_64) | APL_1.16.0.zip |
| macOS binary (arm64) | APL_1.16.0.tgz |
| macOS binary (x86_64) | APL_1.16.0.tgz |
Dependencies
Depends: R (>= 4.4.0)
Imports: Matrix, RSpectra, ggrepel, ggplot2, viridisLite, plotly, SeuratObject, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang
Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, sparseMatrixStats, testthat