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scDD

Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

Bioconductor version: 3.23 · Package version: 1.36.0

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

Installation

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

BiocManager::install("scDD")

Details

MaintainerKeegan Korthauer <keegan@stat.ubc.ca>
AuthorKeegan Korthauer [cre, aut] (ORCID: <https://orcid.org/0000-0002-4565-1654>)
LicenseGPL-2
URLhttps://github.com/kdkorthauer/scDD
Bug Reportshttps://github.com/kdkorthauer/scDD/issues
Downloads rank692
Source branchRELEASE_3_23
biocViewsBayesian, Clustering, DifferentialExpression, ImmunoOncology, MultipleComparison, RNASeq, SingleCell, Software, Visualization

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packagescDD_1.36.0.tar.gz
Windows binary (x86_64)scDD_1.36.0.zip
macOS binary (arm64)scDD_1.36.0.tgz
macOS binary (x86_64)scDD_1.36.0.tgz
Dependencies

Depends: R (>= 3.5.0)

Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran

Suggests: BiocStyle, knitr, gridExtra

Reverse dependencies

Suggests Me (1): splatter