NewWave
Negative binomial model for scRNA-seq
Bioconductor version: 3.23 · Package version: 1.22.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("NewWave")Details
| Maintainer | Federico Agostinis <federico.agostinis@outlook.com> |
| Author | Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] |
| License | GPL-3 |
| Bug Reports | https://github.com/fedeago/NewWave/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | BatchEffect, Coverage, GeneExpression, Regression, Sequencing, SingleCell, Software, Transcriptomics |
| Package Short Url | https://bioconductor.org/packages/NewWave/ |
Citation
From within R, enter citation("NewWave"):
Federico Agostinis, Chiara Romualdi, Gabriele Sales, Davide Risso. NewWave: Negative binomial model for scRNA-seq. doi:10.18129/B9.bioc.NewWave, R package version 1.22.0, https://bioconductor.org/packages/NewWave.
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 | NewWave_1.22.0.tar.gz |
| Windows binary (x86_64) | NewWave_1.22.0.zip |
| macOS binary (arm64) | NewWave_1.22.0.tgz |
| macOS binary (x86_64) | NewWave_1.22.0.tgz |
Dependencies
Depends: R (>= 4.0), SummarizedExperiment
Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats
Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr