This is the development version of ROSeq; for the stable release version, see ROSeq.
Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq dataBioconductor version: Development (3.22)
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, entercitation("ROSeq")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("ROSeq")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ROSeq")Follow Installation instructions to use this package in your R session.
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