augere.solo
Automatic Generation of Single-Cell Analyses
Bioconductor version: 3.24 · Package version: 0.99.3
Implements pipelines for generating single-cell analysis reports in the augere framework. This uses scrapper to execute routine steps such as quality control, normalization, feature selection, clustering and marker detection. We also implement a pipeline for automatic cell type annotation against a labelled reference with SingleR. Each pipeline function generates a self-contained Rmarkdown report with all of the steps required to reproduce its analysis.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("augere.solo")Details
| Maintainer | Aaron Lun <infinite.monkeys.with.keyboards@gmail.com> |
| Author | Aaron Lun [cre, aut] (ORCID: <https://orcid.org/0000-0002-3564-4813>) |
| License | MIT + file LICENSE |
| URL | https://github.com/augere-bioinfo/augere.solo |
| Bug Reports | https://github.com/augere-bioinfo/augere.solo/issues |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | ReportWriting, SingleCell, Software, WorkflowManagement |
| Package Short Url | https://bioconductor.org/packages/augere.solo/ |
Citation
From within R, enter citation("augere.solo"):
Aaron Lun. augere.solo: Automatic Generation of Single-Cell Analyses. doi:10.18129/B9.bioc.augere.solo, R package version 0.99.3, https://bioconductor.org/packages/augere.solo.
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 | augere.solo_0.99.3.tar.gz |
| Windows binary (x86_64) | augere.solo_0.99.3.zip |
| macOS binary (arm64) | augere.solo_0.99.3.tgz |
| macOS binary (x86_64) | augere.solo_0.99.3.tgz |
Dependencies
Imports: augere.core, scrapper, scater
Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, SingleCellExperiment, scRNAseq, SingleR, celldex, jsonlite