Package: ROSeq 1.24.0

Krishan Gupta

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

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.

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

ROSeq_1.24.0.tar.gz
ROSeq_1.24.0.zip(r-4.7)ROSeq_1.24.0.zip(r-4.6)ROSeq_1.24.0.zip(r-4.5)
ROSeq_1.24.0.tgz(r-4.6-any)ROSeq_1.24.0.tgz(r-4.5-any)
ROSeq_1.24.0.tar.gz(r-4.7-any)ROSeq_1.24.0.tar.gz(r-4.6-any)
ROSeq_1.24.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ROSeq/json (API)
NEWS

# Install 'ROSeq' in R:
install.packages('ROSeq', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/krishan57gupta/roseq/issues

Datasets:

On BioConductor:ROSeq-1.25.0(bioc 3.24)ROSeq-1.24.0(bioc 3.23)

geneexpressiondifferentialexpressionsinglecellcount-datagene-expressiongene-expression-profilesnormalizationpopulationsranktmmtungtung-datasettutorialvignette

4.41 score 2 stars 13 scripts 369 downloads 2 exports 6 dependencies

Last updated from:3437ff3ad1 (on RELEASE_3_23). Checks:8 WARNING, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksWARNING142
linux-devel-x86_64WARNING290
source / vignettesOK324
linux-release-x86_64WARNING244
macos-release-arm64WARNING206
macos-oldrel-arm64WARNING189
windows-develWARNING406
windows-releaseWARNING396
windows-oldrelWARNING336
wasm-releaseOK119

Exports:ROSeqTMMnormalization

Dependencies:edgeRlatticelimmalocfitpbmcapplystatmod

ROSeq

Rendered fromROSeq.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2021-02-16
Started: 2019-12-04

Readme and manuals

Help Manual

Help pageTopics
Computes differential expression for the gene in question, by comparing the optimal parameters for sub-populations one and twocomputeDEG
Finds the optimal values of parameters a and b that model the probability distribution of ranks, by Maximising the Log-LikelihoodfindParams
Finds the double derivative of Agetd
Evaluates statistics of the read counts corresponding to the genegetDataStatistics
Finds the first derivative of u1 with respect to a. This first derivative is evaluated at the optimal (a_hat, b_hat).getdu1da
Finds the first derivative of u1 with respect to b. This first derivative is evaluated at the optimal (a_hat, b_hat).getdu1db
Finds the first derivative of u2 with respect to a. This first derivative is evaluated at the optimal (a_hat, b_hat).getdu2da
Finds the first derivative of u2 with respect to b. This first derivative is evaluated at the optimal (a_hat, b_hat).getdu2db
Finds the first derivative of v with respect to a. This first derivative is evaluated at the optimal (a_hat, b_hat).getdvda
Finds the first derivative of v with respect to b. This first derivative is evaluated at the optimal (a_hat, b_hat).getdvdb
Computes the Fisher Information MatrixgetI
Computes u1getu1
Computes u2getu2
Computes vgetv
Computes differential analysis for a given geneinitiateAnalysis
Single cell samples for DE genes analysisL_Tung_single
Minimizes the Negative Log-Likelihood by iterating across values of parameters a and bminimizeNLL
Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq dataROSeq
TMM Normalization.TMMnormalization