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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-rnaseqcovarimpute 1.8.0
Propagated dependencies: r-rlang@1.1.6 r-mice@3.18.0 r-magrittr@2.0.4 r-limma@3.66.0 r-foreach@1.5.2 r-edger@4.8.0 r-dplyr@1.1.4 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-biobase@2.70.0
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-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-riceprobe 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/riceprobe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type rice
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 Rice\_probe\_tab.

r-rols 3.6.1
Propagated dependencies: r-jsonlite@2.0.0 r-httr2@1.2.1 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: http://lgatto.github.io/rols/
Licenses: GPL 2
Build system: r
Synopsis: An R interface to the Ontology Lookup Service
Description:

The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R.

r-ragene10sttranscriptcluster-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/ragene10sttranscriptcluster.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix ragene10 annotation data (chip ragene10sttranscriptcluster)
Description:

Affymetrix ragene10 annotation data (chip ragene10sttranscriptcluster) assembled using data from public repositories.

r-rnbeads-rn5 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.rn5
Licenses: GPL 3
Build system: r
Synopsis: RnBeads.rn5
Description:

Automatically generated RnBeads annotation package for the assembly rn5.

r-rexposome 1.32.0
Propagated dependencies: r-stringr@1.6.0 r-scatterplot3d@0.3-44 r-scales@1.4.0 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-pryr@0.1.6 r-mice@3.18.0 r-lsr@0.5.2 r-lme4@1.1-37 r-imputelcmd@2.1 r-hmisc@5.2-4 r-gtools@3.9.5 r-gridextra@2.3 r-gplots@3.2.0 r-glmnet@4.1-10 r-ggridges@0.5.7 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-factominer@2.12 r-corrplot@0.95 r-circlize@0.4.16 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/rexposome
Licenses: Expat
Build system: r
Synopsis: Exposome exploration and outcome data analysis
Description:

Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.

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-rtcga-cnv 1.38.0
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.CNV
Licenses: GPL 2
Build system: r
Synopsis: CNV (Copy-number variation) datasets from The Cancer Genome Atlas Project
Description:

Package provides CNV (based on Merge snp) datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. Data format is explained here https://wiki.nci.nih.gov/display/TCGA/Retrieving +Data+Using+the+Data+Matrix. Data from 2015-11-01 snapshot.

r-rtrm 1.48.0
Propagated dependencies: r-rsqlite@2.4.4 r-igraph@2.2.1 r-dbi@1.2.3 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/ddiez/rTRM
Licenses: GPL 3
Build system: r
Synopsis: Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks
Description:

rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks.

r-randrotation 1.22.0
Propagated dependencies: r-rdpack@2.6.4
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/phettegger/randRotation
Licenses: GPL 3
Build system: r
Synopsis: Random Rotation Methods for High Dimensional Data with Batch Structure
Description:

This package provides a collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods.

r-ruvcorr 1.42.0
Propagated dependencies: r-snowfall@1.84-6.3 r-reshape2@1.4.5 r-psych@2.5.6 r-mass@7.3-65 r-lattice@0.22-7 r-gridextra@2.3 r-corrplot@0.95 r-bladderbatch@1.48.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RUVcorr
Licenses: GPL 2
Build system: r
Synopsis: Removal of unwanted variation for gene-gene correlations and related analysis
Description:

RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data.

r-rtn 2.34.0
Propagated dependencies: r-viper@1.44.0 r-summarizedexperiment@1.40.0 r-snow@0.4-4 r-s4vectors@0.48.0 r-reder@3.6.0 r-pwr@1.3-0 r-pheatmap@1.0.13 r-mixtools@2.0.0.1 r-minet@3.68.0 r-limma@3.66.0 r-iranges@2.44.0 r-igraph@2.2.1 r-data-table@1.17.8 r-car@3.1-3
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: http://dx.doi.org/10.1038/ncomms3464
Licenses: Artistic License 2.0
Build system: r
Synopsis: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons
Description:

This package provides a transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.

r-rsemmed 1.20.0
Propagated dependencies: r-stringr@1.6.0 r-magrittr@2.0.4 r-igraph@2.2.1 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/lmyint/rsemmed
Licenses: Artistic License 2.0
Build system: r
Synopsis: An interface to the Semantic MEDLINE database
Description:

This package provides a programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths.

r-regparallel 1.28.0
Propagated dependencies: r-survival@3.8-3 r-stringr@1.6.0 r-iterators@1.0.14 r-foreach@1.5.2 r-doparallel@1.0.17 r-data-table@1.17.8 r-arm@1.14-4
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/kevinblighe/RegParallel
Licenses: GPL 3
Build system: r
Synopsis: Standard regression functions in R enabled for parallel processing over large data-frames
Description:

In many analyses, a large amount of variables have to be tested independently against the trait/endpoint of interest, and also adjusted for covariates and confounding factors at the same time. The major bottleneck in these is the amount of time that it takes to complete these analyses. With RegParallel, a large number of tests can be performed simultaneously. On a 12-core system, 144 variables can be tested simultaneously, with 1000s of variables processed in a matter of seconds via nested parallel processing. Works for logistic regression, linear regression, conditional logistic regression, Cox proportional hazards and survival models, and Bayesian logistic regression. Also caters for generalised linear models that utilise survey weights created by the survey CRAN package and that utilise survey::svyglm'.

r-rdrtoolbox 1.60.0
Propagated dependencies: r-rgl@1.3.31 r-mass@7.3-65
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RDRToolbox
Licenses: GPL 2+
Build system: r
Synopsis: package for nonlinear dimension reduction with Isomap and LLE.
Description:

This package provides a package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset.

r-rnamodr-ribomethseq 1.24.0
Propagated dependencies: r-s4vectors@0.48.0 r-rnamodr@1.24.0 r-iranges@2.44.0 r-gviz@1.54.0 r-genomicranges@1.62.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/FelixErnst/RNAmodR.RiboMethSeq
Licenses: Artistic License 2.0
Build system: r
Synopsis: Detection of 2'-O methylations by RiboMethSeq
Description:

RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data.

r-rae230b-db 3.13.0
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/rae230b.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix Affymetrix RAE230B Array annotation data (chip rae230b)
Description:

Affymetrix Affymetrix RAE230B Array annotation data (chip rae230b) assembled using data from public repositories.

r-rificomparative 1.10.0
Propagated dependencies: r-writexl@1.5.4 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-nnet@7.3-20 r-lsd@4.1-0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-foreach@1.5.2 r-egg@0.4.5 r-dta@2.56.0 r-dplyr@1.1.4 r-domc@1.3.8 r-devtools@2.4.6 r-cowplot@1.2.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/rifiComparative
Licenses: FSDG-compatible
Build system: r
Synopsis: 'rifiComparative' compares the output of rifi from two different conditions
Description:

rifiComparative is a continuation of rifi package. It compares two conditions output of rifi using half-life and mRNA at time 0 segments. As an input for the segmentation, the difference between half-life of both condtions and log2FC of the mRNA at time 0 are used. The package provides segmentation, statistics, summary table, fragments visualization and some additional useful plots for further anaylsis.

r-rwgcod-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/rwgcod.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Codelink Rat Whole Genome Bioarray (~34 000 rat gene targets) annotation data (chip rwgcod)
Description:

Codelink Rat Whole Genome Bioarray (~34 000 rat gene targets) annotation data (chip rwgcod) assembled using data from public repositories.

r-rnbeads-hg38 1.42.1
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.hg38
Licenses: GPL 3
Build system: r
Synopsis: RnBeads.hg38
Description:

RnBeads annotation package for the assembly hg38.

r-rae230acdf 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/rae230acdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: rae230acdf
Description:

This package provides a package containing an environment representing the RAE230A.CDF file.

r-rlmm 1.72.0
Propagated dependencies: r-mass@7.3-65
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: http://www.stat.berkeley.edu/users/nrabbee/RLMM
Licenses: LGPL 2.0+
Build system: r
Synopsis: Genotype Calling Algorithm for Affymetrix SNP Arrays
Description:

This package provides a classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.

r-rbcbook1 1.78.0
Propagated dependencies: r-rpart@4.1.24 r-graph@1.88.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: http://www.biostat.harvard.edu/~carey
Licenses: Artistic License 2.0
Build system: r
Synopsis: Support for Springer monograph on Bioconductor
Description:

tools for building book.

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Total results: 68658