R-universe search: exports:plotMA

Showing 9 of total 9 results (show query)

bioc

DESeq2:Differential gene expression analysis based on the negative binomial distribution

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Maintained by Michael Love. Last updated 25 days ago.

sequencing rnaseq chipseq geneexpression transcription normalization differentialexpression bayesian regression principalcomponent clustering immunooncology openblas cpp

375 stars 16.11 score 17k scripts 115 dependents

bioc

BiocGenerics:S4 generic functions used in Bioconductor

The package defines many S4 generic functions used in Bioconductor.

Maintained by Hervé Pagès. Last updated 2 months ago.

infrastructure bioconductor-package core-package

12 stars 14.22 score 612 scripts 2.2k dependents

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 12 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

13.81 score 16k scripts 586 dependents

bioc

geneplotter:Graphics related functions for Bioconductor

Functions for plotting genomic data

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

visualization

8.40 score 249 scripts 10 dependents

bioc

DEXSeq:Inference of differential exon usage in RNA-Seq

The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

Maintained by Alejandro Reyes. Last updated 30 days ago.

immunooncology sequencing rnaseq differentialexpression alternativesplicing differentialsplicing geneexpression visualization

7.75 score 330 scripts 6 dependents

simonlabcode

bakR:Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.

Maintained by Isaac Vock. Last updated 4 months ago.

cpp

6 stars 6.12 score 21 scripts

tinnlab

RCPA:Consensus Pathway Analysis

Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.

Maintained by Ha Nguyen. Last updated 5 months ago.

biobase deseq2 geoquery edger limma rcyjs fgsea browserviz summarizedexperiment annotationdbi rontotools

1 stars 5.50 score 70 scripts

bioc

INSPEcT:Modeling RNA synthesis, processing and degradation with RNA-seq data

INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data.

Maintained by Stefano de Pretis. Last updated 5 months ago.

sequencing rnaseq generegulation timecourse systemsbiology

4.38 score 9 scripts

bioc

codelink:Manipulation of Codelink microarray data

This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software.

Maintained by Diego Diez. Last updated 5 months ago.

microarray onechannel dataimport preprocessing

4.20 score 5 scripts