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ROSeq

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

Bioconductor version: 3.24 · Package version: 1.25.0

ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used.

Installation

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

BiocManager::install("ROSeq")

Details

MaintainerKrishan Gupta <krishang@iiitd.ac.in>
AuthorKrishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut]
LicenseGPL-3
URLhttps://github.com/krishan57gupta/ROSeq
Bug Reportshttps://github.com/krishan57gupta/ROSeq/issues
Downloads rank483
Source branchdevel
biocViewsDifferentialExpression, GeneExpression, SingleCell, Software

Documentation

Download

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

Source packageROSeq_1.25.0.tar.gz
Windows binary (x86_64)ROSeq_1.25.0.zip
macOS binary (arm64)ROSeq_1.25.0.tgz
macOS binary (x86_64)ROSeq_1.25.0.tgz
Dependencies

Depends: R (>= 4.0)

Imports: pbmcapply, edgeR, limma

Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics