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Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

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r-rlassocox 1.18.0
Propagated dependencies: r-survival@3.8-3 r-matrix@1.7-4 r-igraph@2.2.1 r-glmnet@4.1-10
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RLassoCox
Licenses: Artistic License 2.0
Build system: r
Synopsis: reweighted Lasso-Cox by integrating gene interaction information
Description:

RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.

r-rawdiag 1.6.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/fgcz/rawDiag/
Licenses: GPL 3
Build system: r
Synopsis: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful
Description:

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) poses a nontrivial challenge. The rawDiag package facilitates rational method optimization by generating MS operator-tailored diagnostic plots of scan-level metadata. The package is designed for use on the R shell or as a Shiny application on the Orbitrap instrument PC.

r-rnaseqcovarimpute 1.8.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/brennanhilton/RNAseqCovarImpute
Licenses: GPL 3
Build system: r
Synopsis: Impute Covariate Data in RNA Sequencing Studies
Description:

The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR).

r-rattoxfxprobe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/rattoxfxprobe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type rattoxfx
Description:

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was RatToxFX\_probe\_tab.

r-rbwa 1.14.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/Jfortin1/Rbwa
Licenses: Expat
Build system: r
Synopsis: R wrapper for BWA-backtrack and BWA-MEM aligners
Description:

This package provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines.

r-rebet 1.28.0
Propagated dependencies: r-asset@2.28.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/REBET
Licenses: GPL 2
Build system: r
Synopsis: The subREgion-based BurdEn Test (REBET)
Description:

There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions.

r-reducedexperiment 1.2.0
Propagated dependencies: r-wgcna@1.73 r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-rcolorbrewer@1.1-3 r-pheatmap@1.0.13 r-patchwork@1.3.2 r-msigdbr@25.1.1 r-moments@0.14.1 r-lmertest@3.1-3 r-lme4@1.1-37 r-ica@1.0-3 r-ggplot2@4.0.1 r-clusterprofiler@4.18.2 r-car@3.1-3 r-biomart@2.66.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/jackgisby/ReducedExperiment
Licenses: GPL 3+
Build system: r
Synopsis: Containers and tools for dimensionally-reduced -omics representations
Description:

This package provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA).

r-roberts2005annotation-db 3.2.3
Propagated dependencies: r-org-hs-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/Roberts2005Annotation.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Roberts2005Annotation Annotation Data (Roberts2005Annotation)
Description:

Roberts2005Annotation Annotation Data (Roberts2005Annotation) assembled using data from public repositories.

r-rat2302cdf 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/rat2302cdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: rat2302cdf
Description:

This package provides a package containing an environment representing the Rat230_2.cdf file.

r-rflomics 1.2.1
Dependencies: python-scikit-learn@1.7.0 python-scipy@1.12.0 python@3.11.14 python-pandas@2.2.3 python-numpy@1.26.4 python-h5py@3.13.0 argparse@1.1.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/RFLOMICS/RFLOMICS
Licenses: Artistic License 2.0
Build system: r
Synopsis: Interactive web application for Omics-data analysis
Description:

R-package with shiny interface, provides a framework for the analysis of transcriptomics, proteomics and/or metabolomics data. The interface offers a guided experience for the user, from the definition of the experimental design to the integration of several omics table together. A report can be generated with all settings and analysis results.

r-rnagilentdesign028282-db 3.2.3
Propagated dependencies: r-org-rn-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RnAgilentDesign028282.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Agilent Chips that use Agilent design number 028282 annotation data (chip RnAgilentDesign028282)
Description:

Agilent Chips that use Agilent design number 028282 annotation data (chip RnAgilentDesign028282) assembled using data from public repositories.

r-regionereloaded 1.12.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/RMalinverni/regioneReload
Licenses: Artistic License 2.0
Build system: r
Synopsis: RegioneReloaded: Multiple Association for Genomic Region Sets
Description:

RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs.

r-rtcga-clinical 20151101.40.0
Propagated dependencies: r-rtcga@1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RTCGA.clinical
Licenses: GPL 2
Build system: r
Synopsis: Clinical datasets from The Cancer Genome Atlas Project
Description:

Package provides clinical datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. Clinical data format is explained here https://wiki.nci.nih.gov/display/TCGA/Clinical+Data+Overview. Data from 2015-11-01 snapshot.

r-rnbeads-mm9 1.42.0
Propagated dependencies: r-genomicranges@1.62.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RnBeads.mm9
Licenses: GPL 3
Build system: r
Synopsis: RnBeads.mm9
Description:

Automatically generated RnBeads annotation package for the assembly mm9.

r-rhdf5client 1.32.0
Propagated dependencies: r-rjson@0.2.23 r-httr@1.4.7 r-delayedarray@0.36.0 r-data-table@1.17.8
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/rhdf5client
Licenses: Artistic License 2.0
Build system: r
Synopsis: Access HDF5 content from HDF Scalable Data Service
Description:

This package provides functionality for reading data from HDF Scalable Data Service from within R. The HSDSArray function bridges from HSDS to the user via the DelayedArray interface. Bioconductor manages an open HSDS instance graciously provided by John Readey of the HDF Group.

r-rbec 1.18.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/Rbec
Licenses: LGPL 3
Build system: r
Synopsis: Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities
Description:

Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples.

r-rtpca 1.20.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/Rtpca
Licenses: GPL 3
Build system: r
Synopsis: Thermal proximity co-aggregation with R
Description:

R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions.

r-ribodipa 1.18.0
Propagated dependencies: r-txdbmaker@1.6.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-reldist@1.7-2 r-rcpp@1.1.0 r-qvalue@2.42.0 r-matrixstats@1.5.0 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-foreach@1.5.2 r-elitism@1.1.1 r-doparallel@1.0.17 r-deseq2@1.50.2 r-data-table@1.17.8 r-biocgenerics@0.56.0 r-biocfilecache@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RiboDiPA
Licenses: LGPL 3+
Build system: r
Synopsis: Differential pattern analysis for Ribo-seq data
Description:

This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.

r-rcellminer 2.32.0
Propagated dependencies: r-stringr@1.6.0 r-shiny@1.11.1 r-rcellminerdata@2.32.0 r-gplots@3.2.0 r-ggplot2@4.0.1 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: http://discover.nci.nih.gov/cellminer/
Licenses: FSDG-compatible
Build system: r
Synopsis: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines
Description:

The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data.

r-rseqan 1.30.0
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RSeqAn
Licenses: Modified BSD
Build system: r
Synopsis: R SeqAn
Description:

Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R.

r-ragene20stprobeset-db 8.8.0
Propagated dependencies: r-org-rn-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/ragene20stprobeset.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix ragene20 annotation data (chip ragene20stprobeset)
Description:

Affymetrix ragene20 annotation data (chip ragene20stprobeset) assembled using data from public repositories.

r-r10kcod-db 3.4.0
Propagated dependencies: r-org-rn-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/r10kcod.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Codelink UniSet Rat I Bioarray (~10 000 rat gene targets) annotation data (chip r10kcod)
Description:

Codelink UniSet Rat I Bioarray (~10 000 rat gene targets) annotation data (chip r10kcod) assembled using data from public repositories.

r-rmmquant 1.28.0
Dependencies: zlib@1.3.1
Propagated dependencies: r-txdb-mmusculus-ucsc-mm9-knowngene@3.2.2 r-tbx20bamsubset@1.46.0 r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-rcpp@1.1.0 r-org-mm-eg-db@3.22.0 r-genomicranges@1.62.0 r-devtools@2.4.6 r-deseq2@1.50.2 r-biocstyle@2.38.0 r-apeglm@1.32.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/Rmmquant
Licenses: GPL 3
Build system: r
Synopsis: RNA-Seq multi-mapping Reads Quantification Tool
Description:

RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way.

r-reactomegsa 1.24.1
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/reactome/ReactomeGSA
Licenses: Expat
Build system: r
Synopsis: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis
Description:

The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.

Total results: 2911