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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.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

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r-diggit 1.42.0
Propagated dependencies: r-viper@1.44.0 r-ks@1.15.1 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/diggit
Licenses: FSDG-compatible
Build system: r
Synopsis: Inference of Genetic Variants Driving Cellular Phenotypes
Description:

Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm.

r-damsel 1.6.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rsubread@2.24.0 r-rsamtools@2.26.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-plyranges@1.30.1 r-patchwork@1.3.2 r-magrittr@2.0.4 r-goseq@1.62.0 r-ggplot2@4.0.1 r-ggbio@1.58.0 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomeinfodb@1.46.0 r-edger@4.8.0 r-dplyr@1.1.4 r-complexheatmap@2.26.0 r-biostrings@2.78.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/Oshlack/Damsel
Licenses: Expat
Build system: r
Synopsis: Damsel: an end to end analysis of DamID
Description:

Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R.

r-diffustats 1.30.0
Propagated dependencies: r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-precrec@0.14.5 r-plyr@1.8.9 r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1 r-expm@1.0-0 r-checkmate@2.3.3
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/diffuStats
Licenses: GPL 3
Build system: r
Synopsis: Diffusion scores on biological networks
Description:

Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking.

r-damirseq 2.22.0
Propagated dependencies: r-sva@3.58.0 r-summarizedexperiment@1.40.0 r-rsnns@0.4-18 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-randomforest@4.7-1.2 r-plyr@1.8.9 r-plsvarsel@0.9.13 r-pls@2.8-5 r-pheatmap@1.0.13 r-mass@7.3-65 r-lubridate@1.9.4 r-limma@3.66.0 r-kknn@1.4.1 r-ineq@0.2-13 r-hmisc@5.2-4 r-ggplot2@4.0.1 r-fselector@0.34 r-factominer@2.12 r-edger@4.8.0 r-edaseq@2.44.0 r-e1071@1.7-16 r-deseq2@1.50.2 r-corrplot@0.95 r-caret@7.0-1 r-arm@1.14-4
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DaMiRseq
Licenses: GPL 2+
Build system: r
Synopsis: Data Mining for RNA-seq data: normalization, feature selection and classification
Description:

The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.

r-dks 1.56.0
Propagated dependencies: r-cubature@2.1.4-1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/dks
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures
Description:

The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated.

r-drosgenome1probe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/drosgenome1probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type drosgenome1
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 DrosGenome1\_probe\_tab.

r-dmrscan 1.32.0
Propagated dependencies: r-seqinfo@1.0.0 r-rcpproll@0.3.1 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-mass@7.3-65 r-iranges@2.44.0 r-genomicranges@1.62.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/christpa/DMRScan
Licenses: GPL 3
Build system: r
Synopsis: Detection of Differentially Methylated Regions
Description:

This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG.

r-dino 1.16.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-scran@1.38.0 r-s4vectors@0.48.0 r-matrixstats@1.5.0 r-matrix@1.7-4 r-biocsingular@1.26.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/JBrownBiostat/Dino
Licenses: GPL 3
Build system: r
Synopsis: Normalization of Single-Cell mRNA Sequencing Data
Description:

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

r-dominoeffect 1.30.0
Propagated dependencies: r-variantannotation@1.56.0 r-summarizedexperiment@1.40.0 r-seqinfo@1.0.0 r-pwalign@1.6.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-data-table@1.17.8 r-biostrings@2.78.0 r-biomart@2.66.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DominoEffect
Licenses: GPL 3+
Build system: r
Synopsis: Identification and Annotation of Protein Hotspot Residues
Description:

The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions.

r-diffcoexp 1.30.0
Propagated dependencies: r-wgcna@1.73 r-summarizedexperiment@1.40.0 r-psych@2.5.6 r-igraph@2.2.1 r-diffcorr@0.4.5 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/hidelab/diffcoexp
Licenses: FSDG-compatible
Build system: r
Synopsis: Differential Co-expression Analysis
Description:

This package provides a tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance.

r-dta 2.56.0
Propagated dependencies: r-scatterplot3d@0.3-44 r-lsd@4.1-0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DTA
Licenses: Artistic License 2.0
Build system: r
Synopsis: Dynamic Transcriptome Analysis
Description:

Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements.

r-divergence 1.26.0
Propagated dependencies: r-summarizedexperiment@1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/divergence
Licenses: GPL 2
Build system: r
Synopsis: Divergence: Functionality for assessing omics data by divergence with respect to a baseline
Description:

This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features.

r-degraph 1.62.0
Propagated dependencies: r-rrcov@1.7-7 r-rgraphviz@2.54.0 r-rbgl@1.86.0 r-r-utils@2.13.0 r-r-methodss3@1.8.2 r-ncigraph@1.58.0 r-mvtnorm@1.3-3 r-lattice@0.22-7 r-kegggraph@1.70.0 r-graph@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DEGraph
Licenses: GPL 3
Build system: r
Synopsis: Two-sample tests on a graph
Description:

DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results.

r-delayedtensor 1.16.0
Propagated dependencies: r-sparsearray@1.10.2 r-s4arrays@1.10.0 r-rtensor@1.4.9 r-matrix@1.7-4 r-irlba@2.3.5.1 r-hdf5array@1.38.0 r-einsum@0.1.2 r-delayedrandomarray@1.18.0 r-delayedarray@0.36.0 r-biocsingular@1.26.1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DelayedTensor
Licenses: Artistic License 2.0
Build system: r
Synopsis: R package for sparse and out-of-core arithmetic and decomposition of Tensor
Description:

DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum.

r-difflogo 2.34.0
Propagated dependencies: r-cba@0.2-25
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/mgledi/DiffLogo/
Licenses: GPL 2+
Build system: r
Synopsis: DiffLogo: A comparative visualisation of biooligomer motifs
Description:

DiffLogo is an easy-to-use tool to visualize motif differences.

r-decontx 1.8.0
Propagated dependencies: r-withr@3.0.2 r-summarizedexperiment@1.40.0 r-stanheaders@2.32.10 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-scater@1.38.0 r-s4vectors@0.48.0 r-rstantools@2.5.0 r-rstan@2.32.7 r-reshape2@1.4.5 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-plyr@1.8.9 r-patchwork@1.3.2 r-mcmcprecision@0.4.2 r-matrix@1.7-4 r-ggplot2@4.0.1 r-delayedarray@0.36.0 r-dbscan@1.2.3 r-celda@1.26.0 r-bh@1.87.0-1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/decontX
Licenses: Expat
Build system: r
Synopsis: Decontamination of single cell genomics data
Description:

This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.

r-diggitdata 1.42.0
Propagated dependencies: r-viper@1.44.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/diggitdata
Licenses: FSDG-compatible
Build system: r
Synopsis: Example data for the diggit package
Description:

This package provides expression profile and CNV data for glioblastoma from TCGA, and transcriptional and post-translational regulatory networks assembled with the ARACNe and MINDy algorithms, respectively.

r-demuxsnp 1.8.0
Propagated dependencies: r-variantannotation@1.56.0 r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-seqinfo@1.0.0 r-matrixgenerics@1.22.0 r-matrix@1.7-4 r-kernelknn@1.1.6 r-iranges@2.44.0 r-ensembldb@2.34.0 r-dplyr@1.1.4 r-demuxmix@1.1.1-1.09a7918 r-class@7.3-23 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/michaelplynch/demuxSNP
Licenses: GPL 3
Build system: r
Synopsis: scRNAseq demultiplexing using cell hashing and SNPs
Description:

This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework.

r-dstruct 1.16.0
Propagated dependencies: r-zoo@1.8-14 r-s4vectors@0.48.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-purrr@1.2.0 r-iranges@2.44.0 r-ggplot2@4.0.1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/dataMaster-Kris/dStruct
Licenses: GPL 2+
Build system: r
Synopsis: Identifying differentially reactive regions from RNA structurome profiling data
Description:

dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al., Genome Biology, 2019 for the underlying method.

r-duoclustering2018 1.28.0
Propagated dependencies: r-viridis@0.6.5 r-tidyr@1.3.1 r-reshape2@1.4.5 r-purrr@1.2.0 r-mclust@6.1.2 r-magrittr@2.0.4 r-ggthemes@5.1.0 r-ggplot2@4.0.1 r-experimenthub@3.0.0 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DuoClustering2018
Licenses: FSDG-compatible
Build system: r
Synopsis: Data, Clustering Results and Visualization Functions From Duò et al (2018)
Description:

Preprocessed experimental and simulated scRNA-seq data sets used for evaluation of clustering methods for scRNA-seq data in Duò et al (2018). Also contains results from applying several clustering methods to each of the data sets, and functions for plotting method performance.

r-degcre 1.6.0
Propagated dependencies: r-txdb-hsapiens-ucsc-hg38-knowngene@3.22.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-qvalue@2.42.0 r-plotgardener@1.16.0 r-org-hs-eg-db@3.22.0 r-iranges@2.44.0 r-interactionset@1.38.0 r-genomicranges@1.62.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/brianSroberts/DegCre
Licenses: Expat
Build system: r
Synopsis: Probabilistic association of DEGs to CREs from differential data
Description:

DegCre generates associations between differentially expressed genes (DEGs) and cis-regulatory elements (CREs) based on non-parametric concordance between differential data. The user provides GRanges of DEG TSS and CRE regions with differential p-value and optionally log-fold changes and DegCre returns an annotated Hits object with associations and their calculated probabilities. Additionally, the package provides functionality for visualization and conversion to other formats.

r-dnea 1.0.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-netgsa@4.0.6 r-matrix@1.7-4 r-janitor@2.2.1 r-igraph@2.2.1 r-glasso@1.11 r-gdata@3.0.1 r-dplyr@1.1.4 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/Karnovsky-Lab/DNEA
Licenses: Expat
Build system: r
Synopsis: Differential Network Enrichment Analysis for Biological Data
Description:

The DNEA R package is the latest implementation of the Differential Network Enrichment Analysis algorithm and is the successor to the Filigree Java-application described in Iyer et al. (2020). The package is designed to take as input an m x n expression matrix for some -omics modality (ie. metabolomics, lipidomics, proteomics, etc.) and jointly estimate the biological network associations of each condition using the DNEA algorithm described in Ma et al. (2019). This approach provides a framework for data-driven enrichment analysis across two experimental conditions that utilizes the underlying correlation structure of the data to determine feature-feature interactions.

r-differentialregulation 2.8.0
Propagated dependencies: r-tximport@1.38.1 r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dorng@1.8.6.2 r-doparallel@1.0.17 r-data-table@1.17.8 r-bandits@1.26.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/SimoneTiberi/DifferentialRegulation
Licenses: GPL 3
Build system: r
Synopsis: Differentially regulated genes from scRNA-seq data
Description:

DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) ambiguous reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).

Total results: 2909