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

Bioconductor version: Release (3.18)

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.

Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut]

Maintainer: Krishan Gupta <krishang at iiitd.ac.in>

Citation (from within R, enter citation("ROSeq")):


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if (!require("BiocManager", quietly = TRUE))


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ROSeq HTML R Script
Reference Manual PDF


biocViews DifferentialExpression, GeneExpression, SingleCell, Software
Version 1.14.0
In Bioconductor since BioC 3.11 (R-4.0) (4 years)
License GPL-3
Depends R (>= 4.0)
Imports pbmcapply, edgeR, limma
System Requirements
URL https://github.com/krishan57gupta/ROSeq
Bug Reports https://github.com/krishan57gupta/ROSeq/issues
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Suggests knitr, rmarkdown, testthat, RUnit, BiocGenerics
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Source Package ROSeq_1.14.0.tar.gz
Windows Binary ROSeq_1.14.0.zip
macOS Binary (x86_64) ROSeq_1.14.0.tgz
macOS Binary (arm64) ROSeq_1.14.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/ROSeq
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/ROSeq
Bioc Package Browser https://code.bioconductor.org/browse/ROSeq/
Package Short Url https://bioconductor.org/packages/ROSeq/
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