Package: ROSeq 1.25.0

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_1.25.0.tar.gz
ROSeq_1.25.0.zip(r-4.7)ROSeq_1.25.0.zip(r-4.6)ROSeq_1.25.0.zip(r-4.5)
ROSeq_1.25.0.tgz(r-4.6-any)ROSeq_1.25.0.tgz(r-4.5-any)
ROSeq_1.25.0.tar.gz(r-4.7-any)ROSeq_1.25.0.tar.gz(r-4.6-any)
ROSeq_1.25.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ROSeq/json (API)

# Install 'ROSeq' in R:

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

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

Datasets:

L_Tung_single - Single cell samples for DE genes analysis

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

geneexpression differentialexpression singlecell count-data gene-expression gene-expression-profiles normalization populations rank tmm tung tung-dataset tutorial vignette

4.72 score 2 stars 26 scripts 2 exports 6 dependencies

Last updated from:92f6e692c1. Checks:8 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	144
linux-devel-x86_64	WARNING	309
source / vignettes	OK	333
linux-release-x86_64	WARNING	328
macos-release-arm64	WARNING	206
macos-oldrel-arm64	WARNING	179
windows-devel	WARNING	343
windows-release	WARNING	357
windows-oldrel	WARNING	345
wasm-release	OK	105

Exports:ROSeq TMMnormalization

Dependencies:edgeR lattice limma locfit pbmcapply statmod

ROSeq

Rendered fromROSeq.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Computes differential expression for the gene in question, by comparing the optimal parameters for sub-populations one and two	computeDEG
Finds the optimal values of parameters a and b that model the probability distribution of ranks, by Maximising the Log-Likelihood	findParams
Finds the double derivative of A	getd
Evaluates statistics of the read counts corresponding to the gene	getDataStatistics
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 Matrix	getI
Computes u1	getu1
Computes u2	getu2
Computes v	getv
Computes differential analysis for a given gene	initiateAnalysis
Single cell samples for DE genes analysis	L_Tung_single
Minimizes the Negative Log-Likelihood by iterating across values of parameters a and b	minimizeNLL
Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data	ROSeq
TMM Normalization.	TMMnormalization