zinbwave
Zero-Inflated Negative Binomial Model for RNA-Seq Data
Bioconductor version: 3.24 · Package version: 1.35.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("zinbwave")Details
| Maintainer | Davide Risso <risso.davide@gmail.com> |
| Author | Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] |
| License | Artistic-2.0 |
| Bug Reports | https://github.com/drisso/zinbwave/issues |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | DimensionReduction, GeneExpression, ImmunoOncology, RNASeq, Sequencing, SingleCell, Software, Transcriptomics |
| Package Short Url | https://bioconductor.org/packages/zinbwave/ |
Citation
From within R, enter citation("zinbwave"):
Davide Risso, Svetlana Gribkova, Fanny Perraudeau, Jean-Philippe Vert, Clara Bagatin. zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data. doi:10.18129/B9.bioc.zinbwave, R package version 1.35.0, https://bioconductor.org/packages/zinbwave.
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 | zinbwave_1.35.0.tar.gz |
| Windows binary (x86_64) | zinbwave_1.35.0.zip |
| macOS binary (arm64) | zinbwave_1.35.0.tgz |
| macOS binary (x86_64) | zinbwave_1.35.0.tgz |
Dependencies
Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment
Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix
Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, sparseMatrixStats
Reverse dependencies
Imports Me (4): benchdamic, clusterExperiment, scBFA, singleCellTK